Hard Rock Mining Research Articles

Mechanochemical Treatment for the Extraction of Lithium from Hard Rock Minerals: A Comprehensive Review

Mechanochemical Treatment for the Extraction of Lithium from Hard Rock Minerals: A Comprehensive Review

Ambient noise surface-wave imaging in a hardrock environment: implications for mineral exploration

Summary The advancement of seismic methods is vital for mineral exploration in the ongoing energy transition. In this study, we investigate the application of ambient noise seismic interferometry and surface-wave analysis to characterize the subsurface in a mineral exploration context. We then confirm the results of the passive seismic investigation through an active source experiment. We collected ambient noise data using a 2D seismic line initially deployed for an active source reflection seismic study. By cross-correlating the signals, we retrieved the surface waves and constructed a 2D shear-wave velocity profile using conventional surface-wave analysis. We utilized the active source data to establish initial assumptions about the surveyed medium and then validated the passive seismic experiment. The passive seismic results are concordant with the active source results and allow for the interpretation of geological contacts and fault zones. Our work demonstrates the potential of passive seismic methods for investigating local tectonic settings and their role in hardrock mineral exploration.

Impact of steel properties on the susceptibility to corrosion of weld mesh and mesh straps

Reinforcement and surface support are part of an integrated system that transfers and shares load until the excavation surface is stabilized or until the ground support system fails. Steel mesh is the most popular primary surface support element in underground hard rock mines in Canada. Mesh straps are a secondary surface support that provide additional containment as they distribute the load between and across the reinforcement elements. This investigation addresses the role of steel properties, and in particular steel chemistry, in the long-term performance of mesh and mesh straps when exposed to an aggressive corrosive environment. It reports on comparative accelerated corrosion studies to compare the resistance to corrosion of different surface support elements. This has significant implications on the choice of surface support and the anticipated rehabilitation requirements.

Behaviour of paddled energy-absorbing rockbolts under complex loading laboratory conditions

Since their introduction in 2010, paddled energy-absorbing rockbolts have been widely used in seismically active hard rock mines. This paper provides new data and reviews previous work to quantify the performance of paddled energy-absorbing rockbolts under controlled laboratory conditions. Of significance is the realization that the typical split location in impact tests, at the centre between two paddle anchor points can at best provide an upper limit value. This inherent variability in performance under different testing configurations should be acknowledged and taken into consideration in the design of ground support in seismic conditions. This paper discusses the reduction in capacity of paddled rockbolts as a function of loading angle from a maximum value during axial tests (0° loading angle) to the lowest value during pure shear (loading angle of 90°). A significant reduction in displacement capacity is observed as the loading angle changes between axial (0°) and 40°. Beyond a 40° loading angle up to shear (90°) loading the reduction in displacement capacity is less significant. Due to the variances in the capacity observed through the variance of the testing configuration: loading mechanism, direction of loading, location / presence of a discontinuity, it must be recognized that results from laboratory-based testing are not independent of the testing configuration. This should be acknowledged when extrapolating anticipated performance in the field.

Aspects of Spodumene Lithium Extraction Techniques

Lithium (Li), a leading cathode material in rechargeable Li-ion batteries, is vital to modern energy storage technology, establishing it as one of the most impactful and strategical elements. Given the surge in the electric car market, it is crucial to improve lithium recovery from its rich mineral deposits using the most effective extraction technique. In recent years, both industry and academia have shown significant interest in Li recovery from various Li-bearing minerals. Of these, only extraction from spodumene has established a reliable industrial production of Li salts. The current approaches for cracking of the naturally occurring, stable α-spodumene structure into a more open structure—β-spodumene—involve the so-called decrepitation process that takes place at extreme temperatures of ~1100 °C. This conversion is necessary, as β-spodumene is more susceptible to chemical attacks facilitating Li extraction. In the last several decades, many techniques have been demonstrated and patented to process hard-rock mineral spodumene. The objective of this review is to present a thorough analysis of significant findings and the enhancement of process flowsheets over time that can be useful for both research endeavors and industrial process improvements. The review focuses on the following techniques: acid methods, alkali methods, carbonate roasting/autoclaving methods, sulfuric acid roasting/autoclaving methods, chlorinating methods, and mechanochemical activation. Recently, microwaves (MWs), as an energy source, have been employed to transform α-spodumene into β-spodumene. Considering its energy-efficient and short-duration aspects, the review discusses the interaction mechanism of MWs with solids, MW-assisted decrepitation, and Li extraction efficiencies. Finally, the merits and/or disadvantages, challenges, and prospects of the processes are summarized.

(Invited) Electroflow: Sustainable Electrochemical Lithium Chemical Production

The demand for lithium has soared in recent years as the adoption of electric vehicles has grown rapidly, and lithium supply will need to grow another ~5x from current day to meet projected demand for lithium in 2030. To keep pace with this exponentially growing demand, novel methods of lithium sourcing are needed. Direct lithium extraction (DLE) from saltwater brines holds promise to unlock the lithium resources needed to power the electric vehicle revolution.Lithium is traditionally sourced via two methods. Open pit hard rock mining is the method which many are familiar with. In this method, rocks are excavated, roasted with sulfuric acid, and the eluent is processed to produce a pure lithium product. This is well known to be environmentally destructive, and it produces larges amounts of waste products. The second method employed to produce lithium chemicals is brine evaporation ponds. In this method, brine is pumped to a surface pond, often multiple acres across, and solar evaporation concentrates the lithium in the brine to the point that it can undergo further processing. This method is extremely land and water intensive, and local residents adjacent to such ponds are increasingly worried about the effects of such methods on local water tables.Electroflow employs a DLE process to convert brine directly into pure, battery grade lithium chemicals via a sustainable electrochemical process. Our electrochemical cell design enables extraction from dilute lithium brine resources with as low as 10 ppm Li, which unlocks huge swaths of new lithium resources across the globe, and specifically in North America. We use lithium-selective electrode materials as the basis for lithium separation from brine solutions, and this allows a new paradigm of DLE processes that have not yet been thoroughly studied. This talk will describe the operation of our technology and explore the impact that it could have at scale.

Evaluation of underground hard rock mine pillar stability using gene expression programming and decision tree‐support vector machine models

AbstractAssessing the stability of pillars in underground mines (especially in deep underground mines) is a critical concern during both the design and the operational phases of a project. This study mainly focuses on developing two practical models to predict pillar stability status. For this purpose, two robust models were developed using a database including 236 case histories from seven underground hard rock mines, based on gene expression programming (GEP) and decision tree‐support vector machine (DT‐SVM) hybrid algorithms. The performance of the developed models was evaluated based on four common statistical criteria (sensitivity, specificity, Matthews correlation coefficient, and accuracy), receiver operating characteristic (ROC) curve, and testing data sets. The results showed that the GEP and DT‐SVM models performed exceptionally well in assessing pillar stability, showing a high level of accuracy. The DT‐SVM model, in particular, outperformed the GEP model (accuracy of 0.914, sensitivity of 0.842, specificity of 0.929, Matthews correlation coefficient of 0.767, and area under the ROC of 0.897 for the test data set). Furthermore, upon comparing the developed models with the previous ones, it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy. This suggests that these models could serve as dependable tools for project managers, aiding in the evaluation of pillar stability during the design and operational phases of mining projects, despite the inherent challenges in this domain.

Study on mechanical behavior of inclined cemented tailings composite backfill (CTCB) under uniaxial compression

The goaf volume in the vacancy method of hard rock mines is significantly larger than the filling capacity, forming layered and inclined planes during the large-scale filling process. This results in unclear mechanical impact mechanisms on the backfill. To investigate the effect of interface inclination angle on the mechanical behavior of backfill, a set of cemented tailings composite backfill (CTCB) samples with interface inclination angles of 15°, 30°, and 45° are prepared. Uniaxial compression tests and digital image correlation techniques are used to investigate the influence of various interface inclination angles on the strength, deformation behavior, and failure mode of the CTCB. The results show that: (1) The uniaxial compressive strength (UCS) of the CTCB weakens with increasing interface inclination angle but consistently shows exponential growth with curing age (CA) regardless of the interface inclination angle. (2) The interface inclination angle controls the behavior and classification characteristics of the stress-strain curve of CTCB samples, and the increase of interface inclination angle weakens the elastic modulus. (3) The interface inclination angle significantly influences the failure mode and classification characteristics of the samples compared to CA and tailing-to-cement ratio (TCR), with shear slip failure induced by the former and axial splitting failure induced by the latter. The research provides a theoretical basis for understanding the mechanical behavior and stability control of inclined CTCB in underground mining.

Investigating the influence of discontinuity parameters on blast-induced fragmentation

ABSTRACT Fragmentation by blasting is a critical process in hard rock mining. The interaction between the rock mass and blast energy influences the outcomes, specifically on the rock mass response to higher stresses and loading rates, and the properties of structures. In this study, LS-DYNA is used to model the nonlinear dynamic fracturing of the rock to understand the effects of rock mass properties on the blast outcomes. The results are verified and compared with the case study, and they show that the formation of damage zones around the charge is highly influenced by rock mass strength and discontinuity properties.

Decision Intelligence-Based Predictive Modelling of Hard Rock Pillar Stability Using K-Nearest Neighbour Coupled with Grey Wolf Optimization Algorithm

Pillar stability is of paramount importance in ensuring the safety of underground rock engineering structures. The stability of pillars directly influences the structural integrity of the mine and mitigates the risk of collapses or accidents. Therefore, assessing pillar stability is crucial for safe, productive, reliable, and profitable underground mining engineering processes. This study developed the application of decision intelligence-based predictive modelling of hard rock pillar stability in underground engineering structures using K-Nearest Neighbour coupled with the grey wolf optimization algorithm (KNN-GWO). Initially, a substantial dataset consisting of 236 different pillar cases was collected from seven underground hard rock mining engineering projects. This dataset was gathered by considering five significant input variables, namely pillar width, pillar height, pillar width/height ratio, uniaxial compressive strength, and average pillar stress. Secondly, the original hard rock pillar stability level has been classified into three types: failed, unstable, and stable, based on the pillar’s instability mechanism and failure process. Thirdly, several visual relationships were established in order to ascertain the correlation between input variables and the corresponding pillar stability level. Fourthly, the entire pillar database was randomly divided into a training dataset and testing dataset with a 70:30 sampling method. Moreover, the (KNN-GWO) model was developed to predict the stability of pillars in hard rock mining. Lastly, the performance of the suggested predictive model was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The findings of the proposed model offer a superior benchmark for accurately predicting the stability of hard rock pillars. Therefore, it is recommended to employ decision intelligence models in mining engineering in order to effectively prioritise safety measures and improve the efficiency of operational processes, risk management, and decision-making related to underground engineering structures.

Determination of Optimal Burden for Ringhole Blasting in Underground Hard Rock Mining Under Varying Geotechnical Properties

Determination of Optimal Burden for Ringhole Blasting in Underground Hard Rock Mining Under Varying Geotechnical Properties

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers.

Occupational Noise Induced Hearing Loss (ONIHL) is one of the most prevalent conditions among mine workers globally. This reality is due to mine workers being exposed to noise produced by heavy machinery, rock drilling, blasting, and so on. This condition can be compounded by the fact that mine workers often work in confined workspaces for extended periods of time, where little to no attenuation of noise occurs. The objective of this research work is to present a preliminary study of the development of a hearing loss, early monitoring system for mine workers. The system consists of a smart watch and smart hearing muff equipped with sound sensors which collect noise intensity levels and the frequency of exposure. The collected information is transferred to a database where machine learning algorithms namely the logistic regression, support vector machines, decision tree and Random Forest Classifier are used to classify and cluster it into levels of priority. Feedback is then sent from the database to a mine worker smart watch based on priority level. In cases where the priority level is extreme, indicating high levels of noise, the smart watch vibrates to alert the miner. The developed system was tested in a mock mine environment consisting of a 67 metres tunnel located in the basement of a building whose roof top represents the "surface" of a mine. The mock-mine shape, size of the tunnel, steel-support infrastructure, and ventilation system are analogous to deep hard-rock mine. The wireless channel propagation of the mock-mine is statistically characterized in 2.4-2.5 GHz frequency band. Actual underground mine material was used to build the mock mine to ensure it mimics a real mine as close as possible. The system was tested by 50 participants both male and female ranging from ages of 18 to 60 years. Preliminary results of the system show decision tree had the highest accuracy compared to the other algorithms used. It has an average testing accuracy of 91.25% and average training accuracy of 99.79%. The system also showed a good response level in terms of detection of noise input levels of exposure, transmission of the information to the data base and communication of recommendations to the miner. The developed system is still undergoing further refinements and testing prior to being tested in an actual mine.

The Effects of Layer-Induced Elastic Anisotropy on Microseismic Monitoring Records in Underground Coal Mines

Real-time microseismic observations are extensively used in underground hard rock mines. However, microseismic monitoring in coal mines is not common. In this study, the features of microseismic (MS) event waveforms were discussed under conditions of thin-layered sedimentary media. The possibility of source mechanism evaluation was considered for the MS event recorded in coal mine. Moment tensor (MT) inversion of the waveforms can be challenging due to influence of thin-layered medium structure. Splitting of shear SH- and SV-waves is one of the characteristic features that affect MS records in coal mines. The effect of splitting was demonstrated using real microseismic data collected by an underground seismic array installed in the Polosukhinskaya coal mine, Russia. Velocities of shear SH- and SV-waves were estimated from travel times. The results reveal significant variations in shear wave velocities composed of a radial anisotropy of 75%, thus confirming the importance of a thin-layered model for moment tensor inversion. The thin-layered model was built for geological conditions at the Polosukhinskaya coal mine and synthetic seismograms were generated using QSEIS (Pyrocko OS). A full moment tensor of a single typical event was found employing the layered 1D velocity model.

Notch formation in vertical excavations in a deep hard rock mine and rock stabilization methodologies

This paper presents details of the early to mid-stage deterioration in the form of notch growth of two 3 m diameter bored raises that were excavated and slashed to 7.8 m to serve as an internal shaft (winze). In addition, the long-term deterioration of a series of ore/waste passes that were excavated with the bottom-up Alimak methodology is investigated. These excavations were completed at depths between 1200 and 1915 m below the surface in a hard rock mine in Sudbury, Ontario, Canada. The ability to see a cross-section of fracturing around the bored raises while slashing into them and observing the progressive growth of fractures was critical for understanding the factors leading to deterioration. The Alimak-driven ore/waste passes were in an advanced state of deterioration, which provided a late-stage notch growth profile that could be compared with the notch growth observed in the bored raises. The methods used to support and stabilize these excavations are detailed; in addition, methodologies to prevent some of the deterioration from occurring are also presented. The findings of this work are useful for understanding the mechanisms driving brittle rock failure in deep mining and subsequently for minimizing or preventing the deterioration from occurring.

Theoretical substantiation of water inflow into the mined-out space of quarries mining hard-rock building materials

This paper covers topical issues of groundwater and surface water inflow depending on the quarry field spatial dimensions, which leads to an increase in the costs of dewatering and drainage. The spatial dimensions of a quarry field are one of the key indicators in determining the appropriate depth of mining the deposits of hard-rock building materials. The theoretical research is performed using the following methods: statistical and analytical research method for analyzing the hydrological characteristics of non-metallic deposits and their parameters; graphic-analytical method for determining the area of the quarry walls from which water flows due to groundwater and atmospheric precipitation; technical-economic method for assessing the patterns of changes in costs for dewatering the quarry depth; and method of options for selecting the type of water drainage equipment. As a result, patterns have been obtained that characterize the change in the normative inflow of atmospheric precipitation depending on the quarry field area and the change in the unit costs for dewatering depending on the type of the studied quarry field. The practical significance of the conducted research is to use the obtained results of developed design solutions, tested and implemented in the scientific-technical (project) documentation for the mining conditions of Sofiivskyi, Mykytivskyi, Boleslavchykskyi, Chaplinskyi, and Lyubimivskyi fields of non-metallic hard-rock minerals.

U-HRMES: Decision theory-based model for appropriate mining equipment selection in underground hard rock stopes

The selection of mining equipment is one of the most important parts for decision-makers, which can affect the mine and the entire planning. Underground mines are usually divided into two categories; hard rock and soft rock, each of which has its equipment according to the type of exploitation method used. There are different ways to make decisions and choose mining equipment. One of the practical approaches can be the use of the Multi-Criteria-Decision-Making models (MCDM). The main aim of this research is to develop a decision support system based on MCDM methods and use FUZZY logic to resolve uncertainties in the Underground-Hard Rock Mining Equipment Selection (U-HRMES). In this paper, the U-HRMES model has been used in Anguran underground lead and zinc mine as a case study. At present, the drilling and loading-hauling equipment of the Angoran mine does not perform properly. Therefore, to improve the performance and speed up the mining operation, it was tried to suggest suitable equipment using the U-HRMES. For this purpose, effective criteria were obtained based on a literature review, and according to the conditions of the Anguran underground lead and zinc mine, these criteria were filtered, and then, Twenty-two subcriteria with four groups of main criteria; technical specifications of the machine, economic and environmental issues, stope conditions and finally management and operational issues were obtained. It should be noted that the types of preference functions in the selection process of underground hard rock mining equipment were determined based on expert groups. Using the U-HRMES, two series of decisions were evaluated for the options of drilling equipment and loading and hauling equipment for the Anguran underground stopes. Finally, single-arm drilling with a score of 0.870 was chosen as the appropriate choice for drilling equipment, and LHD WX04 with a score of 0.252 was selected for loading and hauling equipment. Also, by analyzing the sensibility of the model results based on the change of criteria weights from ±10 % to ±20 %, there was no change in the results. The observations were that the drilling equipment selection has more sensitivity and reaction to changes in weight of the stope conditions. At the same time, the loading and transport equipment selection has this reaction and this sensitivity to changes in the weight of technical specifications of the machine.

Ensemble learning model on Artificial Neural Network - Backpropagation (ANN-BP) architecture for coal pillar stability classification

Pillars are important structural units used to ensure mining safety in underground hard rock mines. Unstable pillars can significantly increase worker safety hazards and sudden roof collapse. Therefore, precise predictions regarding the stability of underground pillars are required. One common index that is often used to assess pillar stability is the Safety Factor (SF). Unfortunately, such crisp boundaries in pillar stability assessment using SF are unreliable. This paper presents a novel application of Artificial Neural Network-Backpropagation (ANN-BP) and Deep Ensemble Learning for pillar stability classification. There are three types of ANN-BP used for the classification of pillar stability distinguished by their activation functions: ANN-BP ReLU, ANN-BP ELU, and ANN-BP GELU. These three activation functions were chosen because they can solve the vanishing gradient problem in ANN-BP. In addition, a Deep Ensemble Learning process was carried out on these three types of ANN-BP to reduce the prediction variance and improve the classification results. This study also presents two labeling alternatives for pillar stability by considering its suitability with the SF. Thus, pillar stability is expanded into four categories: failed with a suitable safety factor, intact with a suitable safety factor, failed without a suitable safety factor, and intact without a suitable safety factor. There are five features used for each model: pillar width, mining height, bord width, depth to floor, and ratio. In constructing the model, the initial dataset is divided into training data, validation data, and testing data. In this case, four type of proportions are used. For training-testing division the proportions are: 80 % : 20 %, 70 % : 30 %, for training-validation-testing division the proportions are: 80 % : 10 % : 10 %, 70 % : 15 % : 15 %. Average accuracy, F1-score, and F2-score from 10 trials were used as performance indicators for each model. The results showed that the ANN-BP model with Ensemble Learning could improve ANN-BP performance with an average accuracy 86.48 % and an F2-score 96.35 % for the category of failed with a suitable safety factor.

Preconditioning optimization of hydraulic fracturing in block caving of hard rock mass

Introduction: Cavability issues often arise in hard rock mines employing the block caving mining method. Hydraulic fracturing stands out as a promising pre-conditioning technology for hard rock masses, with its pre-conditioning effects dependent on variable parameters.Methods: The impact of cluster number, injection rate, and liquid volume on fracture propagation was investigated through on-site hydraulic fracturing practices. Fracturability was initially verified via true triaxial fracturing tests, and preconditioning parameters were subsequently optimized using numerical simulations. The optimized on-site construction was monitored using electromagnetic methods.Results: The study revealed a negative correlation between fracture radius and the number of clusters, gradually decreasing with an increase in cluster number. However, the fracture weakening area increased with the rise in the number of clusters. The fracture weakening area also increased with the injection rate. Additionally, the fracture radius initially increased and then decreased. Both fracture weakening area and radius increased with an increase in liquid volume. The optimized parameters were identified as four clusters, an injection rate of 4 m3/min, and a liquid volume of 500 m3.Discussion: Building upon these findings, on-site experiments were conducted and monitored using electromagnetic methods. The monitored results demonstrated that the optimized parameters effectively achieved hydraulic fracturing expansion, accomplishing the goal of weakening hard rock.

A deep learning benchmark for first break detection from hardrock seismic reflection data

Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. Although many works have shown the benefits of deep learning, assessing the generalization capabilities of proposed methods for data acquired in different conditions and geologic environments remains challenging. This is especially true for applications in hardrock environments. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled data sets and the use of inadequate evaluation methodologies. Because machine learning models are prone to overfit and underperform when the data used to train them are site specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we develop a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey data set acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons and discuss potential improvements to this approach.

Mechanical, energy evolution, damage and microstructural behavior of cemented tailings-rock fill considering rock content and size effects

Cemented tailings-rock fill (CTRF) is crucial for engineering intention and durability of re-filled stopes in deep underground mines. The current work examined the effects of fragment contents and sizes on the mechanical, energy evolution and microstructure of CTRF with 70 wt% solid. All CTRFs were subjected to uniaxial compression, energy dissipation and scanning electron microscopy experiments. Strength outcomes exhibit that adding rock fragments to CTRF had a clear mitigating influence on the backfill strength. Total/elastic-strain energy at peak point decreased with growing rock content in backfill. SEM-EDS studies demonstrate that backfill defects were chiefly micro-porosity and fractures. The findings are of great significance to guarantee the success and security of the CTRF fulfilled in deep underground hard rock and metalliferous mines.