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By Yuan Le, Dominic Reda, Vojtech Ernst Gall, Kurt Zeidler, Tomasz Kecerski

High Speed 2 (HS2) mandated that Building Information Modelling (BIM) processes be implemented during design and construction. Gall Zeidler Consultants (GZ) was responsible for developing the BIM Models for the portals, tunnels, and cross passages of the of the 16 km-long Chiltern twin-bore Tunnel. In addition, GZ developed the GBR for the Chiltern Tunnels and incorporated the relevant information into the Project’s BIM framework. This paper presents the implemented BIM framework, describes the modelling approach, and discusses the advantages and challenges of implementing BIM, particularly how BIM improved interface coordination and how BIM was used in feasibility assessments of different construction options.

Jun 13, 2023

By Nick Karlin

Dragados USA is constructing the LA Effluent Outfall Tunnel, a 7-mile, 18 ft internal diameter effluent discharge tunnel, utilizing a 21.6 ft (6.585 m) Herrenknecht Mixshield TBM. This paper highlights the means and methods used for TBM launch from a 55 ft diameter shaft excavated in wet with diaphragm wall support of excavation. This included SEM excavation of jet grout blocks for starter and tail tunnels, temporary concrete structures and steel installations, TBM sealing system, umbilical management for TBM hydraulic, electric and bentonite circuits as well as complex segment delivery logistics. The many launching challenges were overcome thanks to the innovative approaches from the project team.

Jun 13, 2023

By B. Genc, C. Gous

Spontaneous combustion during coal mining operations is a major problem that affects the health and safety of workers and causes environmental problems. The phenomenon is associated with the presence of coal, coal shale, and pyrite. In 2020, a premature detonation incident occurred at an iron ore mine where the waste material contains black carbonaceous shale units known to be associated with pyrite. The spontaneous combustion propensity and properties of samples of the black carbonaceous shales from the mine were examined and compared with samples from the Witbank Coalfield. The spontaneous combustion liability indexes of these samples were correlated with X-ray fluorescence (XRF) and proximate and ultimate analyses using linear regression. The Wits-Ehac Index classification results show that the samples were between medium and high risk. The linear regression analysis showed very poor correlations between the Wits-Ehac Index results and the XRF and proximate and ultimate results. The most valuable relationship found is between the presence of relatively high sulphur (greater than 3%) and ground reactivity with nitrate-bearing explosive emulsion.

Jun 5, 2023

By M. Z. Emad, N. Abbas, Y. Majeed

Room-and-pillar mining is commonly employed for the extraction of rock salt in undergroundmines. Pillar stress is a major concern in these mines as it is directly related to stability and mineralrecovery. In this study a flat jack method was used to measure pillar stresses in three undergroundrock salt mines in Pakistan. The field work included determination of in-situ stress, in-situ elastic modulus, recording of field variables (pillar length and width, height and width of opening, opening width to height ratio, extraction ratio, and overburden height) and collection of salt block samples. The geomechanical properties of rock salt (uniaxial compressive strength, Young’s modulus, Brazilian tensile strength, and density) were also determined to estimate overburden stress, pillar strength, and factor of safety (both estimated and actual). It was found that the measured pillar stresses are proportional to the overburden stress values, with their magnitude ranging from 6.05 MPa to 11.97 Mpa, and the pillars were found to be stable. Regression analysis was performed to develop statistical models for in-situ stress and in-situ elastic modulus. Finally a quick guideline chart was developed to determine the suitable length of pillar for a given span and required level of safety.

Jun 5, 2023

By K. J. Craig, M. A. Swart, Q. G. Reynolds, L. Hockaday

A solar reflector soiling study was carried out at a ferromanganese smelter in South Africa to assess the soiling rates at different locations around the plant. Several meteorological parameters were monitored to give insight into the conditions that lead to increased soiling. Mineralogical characterization of dust samples collected from the reflectors and the atmosphere revealed that only a certain size fraction is of importance with regard to soiling, and that the dust can be attributed to both raw materials and smelter products. Proximity to the dust source was the primary driver for increased soiling. The site that experienced the most soiling was very close to raw material heaps; this was deemed an outlier and was excluded from the summary statistics. The secondary driver for increased soiling was location relative to the smelter dust sources and the wind’s direction and speed. The reflector set at the best location experienced 13.1% less soiling than the set at the ‘worst’ (but still feasible) location, represented by an averaged mean daily reflectance loss of 0.0186. The study revealed that while there are periods of intense soiling at this particular site, proper planning of reflector location in relation to the smelter dust sources can significantly mitigate the soiling rate.

Jun 5, 2023

By A. T. van Zyl, M. Mthenjane, M. J. Cole

Growing global concern over the impacts of climate change, attributable largely to fossil fuel energy sources, has led to the widely shared goal for a ‘just transition’ to cleaner energy and reduced dependence on carbon-based fuels. As the world’s 14th biggest CO2 emitter and being particularly vulnerable to the impacts of climate change, South Africa must embark on a just transition pathway. This paper reviews expected coal mine closures and associated community vulnerabilities and local governance challenges in South Africa. Decommissioning schedules for all coal-fired power stations and operating coal mines are plotted, and 69 mining host communities and 21 municipalities are mapped, classified, and described. Community socio-economic profiles are measured using a set of SDG indicators and census data and municipalities assessed through financial audits. Our research shows that five coal-fired power plants (8.9 GW) and 15 coal mines (29.5 Mt/a) will probably close by 2030, and a further four plants (14 GW) and 23 mines (106 Mt/a) by 2040. Thus, the shift to cleaner energy will likely occur without the premature closures implied by the just transition. The impact of mine closure on the 2.5 million residents of host communities will be significant, particularly as levels of income, employment, and education are already very low and many municipalities are in financial distress. The South African approach to the just transition needs to take local realities into account and the narrative must support an effective transition that does not undermine energy security and economic growth.

Jun 5, 2023

By K. Woodward, Y. Potvin, B. McFadyen, M. Grenon

Open stoping has become a popular mining method in hard rock mines, not only due to the safety of the method as a non-entry approach, but also because of the high extraction rate and low costs. At mine sites, stope performance is evaluated by calculating stope overbreak using the stability chart. However, limitations of the stability chart regarding the precision of the predictions, nonconsideration of factors such as the influence of blasting, and the exclusion of underbreak have led to non-optimal designs. The capabilities of today's computers have increased the amount of data being collected and the power of models being built. This article presents a step towards a new stope design approach where stope overbreak and underbreak are measured and georeferenced using octrees at an approximately cubic metre resolution and predicted using multivariate statistical models (partial least square, linear discriminant analysis, and random forest). Results show that overbreak and underbreak location along the design surface and their magnitude are predicted with good precision using a random forest model. These predictions are used to build the expected geometry of the open stope. The resolution of the data and the use of multivariate analysis has enabled the prediction of variation in stope performance along the design surface, going well beyond the simple qualitative per stope face prediction provided by a traditional stability chart approach.

Jun 5, 2023

By Wei Victor Liu, Na Zhang, Chengkai Fan, Bei Jiang

This study used artificial neural networks (ANNs) coupled with Gaussian mixture modeling (GMM) to analyze truck haulage data from openpit mines. The proposed weighted ensemble models performed better than benchmark models in predicting truck productivity, providing a new means of solving complex data and improving mine planning and decision-making.

Jun 1, 2023

By Juan J. Monsalve, AMAN SONI, Nino Ripepi, Richard Bishop

The design and strength of pillars are important parameters when implementing the room-and-pillar mining method in a limestone mine. The process becomes complex with the presence of karst voids, which are frequent phenomena in carbonate rock formations. This research continues a previous study aimed at studying the effect of karst voids on the strength of a particular limestone pillar. Using calculated pillar strength and estimated redistributed stresses, it was assessed that the pillar can counter more stress than currently imparted on it. Numerical modeling studies combining in situ stress determination and excavation sequencing were used to estimate the existing stresses on the pillar. Different possibilities of modification to existing pillar design were simulated using distinct element method (DEM) modeling to determine a better design.

Jun 1, 2023

By Irina Chernyshova, Erik Larsen, Hanumantha Rao Kota, Vladislav Slabov, Garima Jain

The mining industry faces pressure to provide metals for the green digital shift using sustainable technologies. This study evaluates biosurfactants as collectors for ultrafine and coarser particles of hematite and malachite, aiming to improve the flotation of ultrafine particles.

Jun 1, 2023

By Yueyue Ding, Yuanhui Li, Zhengrong Li, Pengfei Yu, Kunmeng Li, Zhipeng Xiong

The overburden responses in gently inclined orebody mining are different from those in horizontal orebody affected by the orebody dip angle. In particular, the mining-induced stress presents asymmetrical distribution characteristics spatially, resulting in stress concentration and large-scale pressure relief phenomenon in the surrounding rock. As a result, frequent ground pressure disasters such as roof collapse and barrier splitting occur frequently. Moreover, the ground pressure evolution law and the interaction between stopes in multistope mining are complex. The effective ground pressure control measures cannot be taken due to unclear understanding of the evolution of stress and displacement fields, justifying the investigation of the distribution and evolution of underground pressure for safe and efficient mining.

Jun 1, 2023

By Ihsan Berk Tulu, Haochen Zhao, Deniz Tuncay

The process of designing pillars and selecting support measures in underground coal mines relies highly on the accurate assessment of mining-induced loads and the load-bearing capacities of the supports. This study proposes a new method to estimate the percentage of load carried by the gob in underground coal mines using a new parameter that introduces the site-specific overburden geology.

Jun 1, 2023

By M J. Nimmo

extremely powerful tools for building predictive and generative models. ML can be used for building highly accurate regression and classification models. But without careful data science and statistical analysis, a regression or classification model could be highly biased and completely wrong – and we may not even realise. An example rock density estimation task will be used to illustrate the potential gains and possible pitfalls in using ML and AI in Mineral Resource estimation. What could possibly go wrong? Nothing! For most simple regression or classification problems, very little. For density estimation, using the global mean may be all that is possible, but the resource classification would need to reflect the uncertainty in global estimation of tonnage. Everything! From data collection and measurement, from selecting ML and AI techniques and designing the analysis workflow to our own cognitive bias. The things that could go wrong are vast and may include but not limited to asking the wrong question, not asking questions, data bias, measurement errors, insufficient data coverage, not collecting the right variables, not collecting enough observations, collecting clustered samples, unbalanced data, filtering out data without reason (outliers), clipping data (outliers), relying on automatic feature selection, including irrelevant variables, excluding relevant variables, geological interpretation, changing context, blindly following best practice, splitting small data sets, subsetting large data sets, using the wrong tool for the task, insufficient budget to complete the analysis, not allowing enough time for testing and experimentation (the science), focusing too much on building the model (the engineering), not learning from the data, our assumptions, our preconceptions, and our skill. This paper will explore the question of how training regression models to predict rock density are affected by gaps in the data (missing observations and missing variables). What could possibly go wrong?

May 24, 2023

By W Patton

Investment decisions in the mineral resources sector are made on the basis of an assessment of the economic potential for a mineral deposit. Given the supporting information for the location, scale, and quantity of the commodity of interest, miners and investors aim to make risk-informed decisions when considering further investment. The objective of resource classification is the synthesis of various types and quantities of geologic information used in resource model construction to assign confidence categories to the estimated mineral resources, which can then be used to support informed economic decisions. In this paper a risk-based classification scheme for iron ore mineral resources using conditional simulations is presented. The method involves categorisation of each block according to a confidence level, considering expected recoveries from a kriging model over mining production volumes. The realisations of the simulated model are used to assign risk-based confidence categories to zones of the ore deposit. One motivation for this work is to assess the sensitivity of the scheme to geologic uncertainties. Specifically, that which uncertainty in the layout of mineralising alterations and weathering overprints influence variabilities of associated rock properties and the likelihood of obtaining expected ore recoveries. It is shown these risks have material implications for a project’s economic potential. How the scheme can be extended to the grade control context and the value subsequent grade control activities is also quantified.

May 24, 2023

By L Bertoss, D Carvalho

In the last decade, the use of advanced implicit modelling software/algorithms in mineral resource workflows has become a best practice in the mining industry. In most cases, these workflows can easily replicate the level of detail of explicitly modelled geometries, even for very complex deposits. These techniques are flexible and can produce fast results since software is responsible for generating 3D surfaces and solids based on geological and interpretation data. Nevertheless, depending on how unique and complex some deposits are, intense manual editing and input are required to honour the geological context and produce the desired outcome. The current geological modelling workflow at George Fisher Mine (GFM) is based on the semi-automatic built-in tool that generates stratigraphic sequences (stacked lithology layers) based on logging and mapping data. This tool (stratigraphic sequence from Leapfrog Geo™) works for the majority of areas in the mine but fails to capture observed mineralisation pinch-outs in operational areas and at extrapolated boundaries of the deposit. These pinch-outs have the potential to negatively impact grade forecasts if not resolved manually. Using built-in software tools only, the alternative for a more accurate model is to generate each individual layer and adding tens of thousands of manual interpretation points to honour pinch-outs (based on the amount of existing drilling in the mine and number of layers). This approach would require an estimated minimum of two to four years of a full-time employee (FTE) to be exhaustively completed. This study presents an automated approach to generate pinch-out points using a set of Python scripts that only take a few minutes to run. The newly generated points are then combined into the commercial software workflow as interpretation points, honouring observed pinch-outs. The remarkable time difference between approaches removes chance of manual errors and allows mine geologists to spend valuable time in validation and testing of scenarios, ensuring they are more representative of the deposit’s geology.

May 24, 2023

By H Arvidson, V Chamberlain, R Marinho, B Parsons, T Rowl, M Noppé, M Mattera

With the introduction of CRIRSCO (2019) based mineral asset disclosure, Regulation S-K part 1300, 2019 (S-K1300) by the United States (US) Securities and Exchange Commission (SEC), applicable to companies listed on the New York Stock Exchange from 2021, Mineral Resource is now required to be reported Exclusive of Mineral Reserve, referred to here as ‘Exclusive Mineral Resource’ (EMR). EMR is equivalent to reporting Mineral Resource additional to Mineral Reserve, which has been permitted in jurisdictions outside the US for many years. Despite this, there are no practical guidelines in the literature or relevant reporting codes and rules for Competent/Qualified Persons to refer to, which creates a risk that disclosure of EMR estimates across the industry will be inconsistent and potentially ambiguous for analysts, investors, potential investors, and other stakeholders.With the introduction of CRIRSCO (2019) based mineral asset disclosure, Regulation S-K part 1300, 2019 (S-K1300) by the United States (US) Securities and Exchange Commission (SEC), applicable to companies listed on the New York Stock Exchange from 2021, Mineral Resource is now required to be reported Exclusive of Mineral Reserve, referred to here as ‘Exclusive Mineral Resource’ (EMR). EMR is equivalent to reporting Mineral Resource additional to Mineral Reserve, which has been permitted in jurisdictions outside the US for many years. Despite this, there are no practical guidelines in the literature or relevant reporting codes and rules for Competent/Qualified Persons to refer to, which creates a risk that disclosure of EMR estimates across the industry will be inconsistent and potentially ambiguous for analysts, investors, potential investors, and other stakeholders.

May 24, 2023

By I Sucholutsky, D M. First, D Mogilny, F Yusufali

Machine learning is creating value in all facets of the mining industry, from exploration to production. The authors provide an accessible, high-level introduction to artificial intelligence (AI), machine learning (ML), and deep learning (DL); the latter being widely recognised as one of the most powerful forms of ML. In particular, the authors will introduce deep learning models known as convolutional neural networks (CNNs), how they are applied, and the economic considerations necessary for determining when DL may be the right solution to de-risking complex block modelling problems. The authors present preliminary results from the mineral resource modelling study of the Jundee orogenic gold deposit, Yandal belt/gold province, Western Australia. The primary goal was to identify the direction and location of the narrow gold veins more accurately and demonstrate how non-linearly correlated elements are used as direct inputs to the resource model to assist with the target element’s grade prediction. This demonstrates that: (1) existing techniques for finding correlations between assayed elements do not adequately reflect the complex geology of the asset, (2) nonlinear correlations that are difficult to model as simple mathematical functions are representative of geological patterns in a deposit, and (3) non-linearly correlated assayed data, fed as inputs, increase the performance of the resource model as reconciled through blind tests. To conclude, the authors hypothesise that the patterns represented by the DL block models may be revealing the results of overprinting geological processes that generated mineral deposits. For example, the primary hydrothermal processes that deposited metals created depositional patterns that become particularly complex as a result of being overprinted, in part or in whole, by secondary physicochemical processes. This may explain why non-linear geochemical relationships have the capacity to generate more accurate block models of mineral deposits.

May 24, 2023

By A A. Latscha, D O’Connor

Increasing orebody complexity, restrictions in ground access, and longer lead times for disturbance approvals, have generated the need for the Resource Development Team within Rio Tinto Iron Ore (RTIO), to explore optimised drill design techniques that deliver resource conversion value while maximising program outcomes. In response to this challenge, the Team initiated a case study to produce an AI assisted/designed drill program using a non-machine learning AI. The case study involved the assessment and implementation of an AI generated drill program at one of RTIO’s Pilbara iron ore deposits, located in the Hamersley Province of Western Australia. The Mineral Resource is currently classified as 100 per cent Inferred. Access constraints inherent to topography, and the presence of exclusions zones have prevented adequate drilling coverage, resulting in lower resource confidence and a poor definition of the orebody extents and geometry. The drill campaign, planned in Q1 2023, needs to consider access constraints, complex topology and the limitations related to additional ground clearance, while addressing the requirement of improving orebody knowledge. The drill plan had to address two objectives. The first and main aim of drilling was to upgrade the Resource Classification to allow for possible conversion to Ore Reserve. The input for this objective was a volumetric representation of the areas currently not meeting pre-defined quantitative Resource Classification upgrade criteria. The second objective was to better define the orebody extents and geometry. For the second objective, the input to the drilling optimiser was a volumetric representation (blocks) of the areas of high uncertainty in the mineralisation/waste boundary, which were defined using an indicator conditional simulation technique. The drilling optimiser output was a range of compliant drill plans. A set of KPIs based on economic and orebody knowledge criteria were used to assess the plans and to select the most optimal drill design to be drilled in 2023. The geology and management teams were able to make better informed, objective decisions using the trade-off between cost and orebody knowledge gained. As part of the assessment, the AI generated plans were compared to our current planning practises. The AI assisted plans, when gauged against the project KPIs, showed clear and measurable improvements in both meeting project objectives and in managing operational risk.

May 24, 2023

By D Clarke, I Minniakhmetov

This paper describes the application of the SBRE framework to model the Olympic Dam deposit, one of the largest copper and uranium deposits in the world. Geostatistical simulations are the best practice for quantifying uncertainty in the mineral value chain and estimating resources. However, these simulations are currently undertaken on an ad hoc basis in the mining industry due to the high requirements in computing resources and capabilities in advanced geostatistics. The SBRE project provides a standard framework to test, build, and run conditional simulation workflows, which has been applied to the Olympic Dam deposit. Conventional geostatistical methods face challenges when modelling the Olympic Dam deposit, such as non-stationarity, non-linear correlations, and a large number of grid blocks to simulate. To address these challenges, a combination of methods was used, including projection pursuit multivariate transform, trend-residual decorrelation, pluri-Gaussian simulations, and turning-bands simulations. Various tools and applications were used to build the model, including in-house developed applications, Autovariogram software (Varify), and the RMSP python library. All computations were accomplished on the AWS cloud using the EC2-Batch service. The final set of simulations was validated using various methods, such as grade-tonnage curves, histograms, swath plots, and variogram reproductions, as well as validation using historical samples back-testing. The study highlights the importance of an automated, modular, and scalable framework in the development of conditional simulation workflows and describes the learning gained from trials of different combinations of methods and software

May 24, 2023

By C Artica

Two quantitative methods for Mineral Resource classification have been applied to a copper skarn deposit beneath a large open pit that is mining a world-class porphyry complex. A drill hole spacing study (DHSS) using the single block kriging method (SBK) and a conditional simulation method (CS) were applied to the skarn deposit and the results compared. Inputs to both processes included the proposed mining rate and stope designs to estimate the likelihood of achieving grade and metal production targets. Mineral Resource classification of the deposit considers the relative precision of tons, grade and metal predictions over quarterly and annual production periods. These relative precision estimates are calculated at 90 per cent confidence interval (CI). The SBK method represented the production period as a single large block. Kriging estimates were performed in the large block for various synthetic drill hole grids, and the estimation errors derived. The error was assumed to be normally distributed due to the large block size and quantity of samples, allowing CIs to be deduced. The CS method followed a conventional sequential Gaussian simulation workflow where grades were simulated with a 5 ft × 5 ft × 5 ft grid node spacing inside mineralisation domains. Each realisation was then averaged up to monthly production volumes represented by a combination of designed stopes, and the coefficient of variation (CV) for the averaged grade was calculated. CI on the relative precision of grade predictions were then deduced from the CVs. The two methods produced similar results; despite being calculated independently. CS results were used to report uncertainty in the proposed life-of-mine plan and highlight areas for additional drilling. SBK results were used as the basis for Mineral Resource classification as this method has been previously applied to the skarn deposit.

May 24, 2023