ABSTRACTThis paper presents an approach to optimise the long-term production scheduling of an open pit mine with multiple processing streams, while accounting for investment decisions under mineral supply uncertainty. The solution approach consists of first solving the linear relaxation using an extension of the Bienstock-Zuckerberg algorithm to the stochastic optimisation. Then, a rounding heuristic based on the topological sorting is applied, followed by a parallel multi-neighbourhood Tabu search. The proposed method is applied to a multi-product open pit mine deposit, with the possibility of investing in new shovels, trucks or crushers to increase related capacities.

ABSTRACTReducing the footprint of mining by backfilling and rehabilitating open pit mines progressively reduces the amount of land exposed at any point in time. Optimal dump destination (in-pit or external) depends on their availability, which for in-pit is determined by the mined-out void created and available lag distance between the mining face to the dumping face. A mathematical model has been developed to maximise value of the mine while not only considering external dumping option but in-pit dumping as well while maintaining the lag distance with the working face. The model has been applied to a stratified deposit mine with promising results.

ABSTRACTIn recent years, several mining companies worldwide have made efforts towards establishing sustainable value-adding rare earth element (REE) supply chains to compete with China. Lynas and Iluka are two Australian mining companies that recently announced their plans to construct REE refinery plants to process and separate REE oxides in Western Australia and Texas, US. Though promising, such investments entail high risks. This work discusses the status of the Australian REE industry and uses SWOT and risk analyses to identify and evaluate the challenges and opportunities with respect to building an integrated rare earth supply chain outside of China.

ABSTRACTTunnel failure caused due to geologic heterogeneity and variability in both mining processes and tunnel arrangement in deep metal mines is very complex. In this paper, a quantitative risk assessment for deep tunnel failure was conducted using clustering and logistic regression. The study was performed by considering various representative evaluation indexes of geological conditions, mining disturbance, and microseismic (MS) data. Comprehensive Seismic Event Clustering (CSEC) methodology was used to analyse the MS data to filter noise data and identify seismic sources. Additionally, peak particle velocity (PPV) was used to quantitatively evaluate the influence of MS activities on the response of tunnel which would aid in determining the PPV distribution characteristics of a large regional tunnel. Furthermore, Excavation Vulnerability Potential (EVP) index was used to quantitatively evaluate the effect of site conditions on response of the tunnel. Thus, based on logistic regression, PPV and EVP were used to establish a risk assessment model to determine the tunnel damage levels and probability of damage occurrence. The successful application of the proposed method at the Ashele copper mine demonstrates that it is capable of conducting a risk assessment for tunnel failure in deep metal mines.

ABSTRACTCovid-19 has damaged the global economy, as the series of lockdowns had negative impacts on the global economy. Investors around the world invested heavily in gold, and as a result, steep increase occurred. This study was intended to determine the relationship between COVID-19 infection rates and the price of gold, using different statistical analyses. Johansen cointegration test revealed that cointegration existed from a lag of 0 to 21 days, yet no relationship was observed from day 22 onwards. Also, Granger causality test revealed that gold price had a significant response to increasing COVID-19 cases. Conversely, gold price did not have Granger causes on the COVID-19 infection rates. Furthermore, the Vector Error Correction Model (VECM) result indicated that a 1% increase in COVID-19 cases led to a 0.0166% increase in gold price. Considering the exponential growth in COVID-19 cases of 17.38% average growth per day during the period from 1 January 2020 to 31 March 2022, 0.2885% increase in gold price per day has been attributed from COVID-19 cases. The sequential methodology presented in this paper is useful to potential gold investors in understanding the responsiveness of gold price to global pandemics and forecasting future gold prices during such global crises.

ABSTRACTThis work deals with the optimal decisions for materials handling in open-pit mines. The main purpose of this research is to present a multi-stage approach, which has three sequential stages: (a) fleet size selection stage, (b) fleet allocation stage, and (c) real-time truck dispatching stage. At first, a simulation-based optimisation method is provided to evaluate the optimal number of trucks. Then, a new heuristic algorithm is developed for making operational decisions. To evaluate the performance of the proposed framework, the dispatching algorithm used by Modular Mining DISPATCH is developed. The results indicate that the proposed method yields fairly good performance.

ABSTRACTThis paper overviews the simultaneous stochastic optimisation of mining complexes or mineral value chains where raw materials mined from mineral deposits in an area are transformed into a set of sellable products. The supply of materials extracted from available mines represents a major source of uncertainty and technical risk that needs to be managed, along with market demand. An overview of the main concepts, case studies and comparisons show how the approach manages risk and capitalises on synergies between the components of the mining complex and major differences from conventional methods. Results lead to strategic plans with larger amounts of metal produced from the same mineral resources, a substantially improved ability for operations to meet production forecasts, and a significantly higher net present value.

ABSTRACTOn mine waste rock slopes, trees with inadequate root development could be prone to uprooting. The anchorage of trees is mainly determined by the architecture of the root systems that drive their mechanical interactions with the soil. The objective of this study was to evaluate the effects of soil quality and of different planting materials on the architecture and resistance to shear stress of root systems of hybrid poplars four years after they were initially planted. The study was conducted in an open-pit-gold mine. A hybrid poplar plantation was established in 2013 on 33% soil-covered waste rock slopes, using a randomised complete block design, that is, 3 replicated blocks × 3 planting materials × 2 soil qualities. The stability of the hybrid poplars (resistance to uprooting) was evaluated using lateral traction tests. Complete excavations were performed to characterise their coarse root (>4 mm) architecture. Results showed no significant differences between treatments in terms of the maximum resistance force to uprooting, which varied between 7142 and 8989 N. After four growing seasons, no significant effects due to soil quality or planting material were observed in the number of lateral roots, mean root diameter, root biomass, aboveground biomass, and shoot/root ratio.

ABSTRACTTo characterise the relationships among the hazards associated with tailings storage facility (TSF) dam failure, interpretive structural modelling (ISM) is used to hierarchise, structuralise and visualise them. Based on evidence, 15 hazards in four categories are identified, and are classified into seven levels and four clusters. The results show that seven hazards can be regarded as the fundamental hazards. The majority of hazards associated with technology factor can be seen as transactional hazards and are closely related to the water balance problem. The proposed ISM method offers theoretical reference and improves risk management for the TSF and the mining industry.

ABSTRACTThis study focuses on addressing the issues of spontaneous combustion and slope instability at the coal mine waste (CMW) dump located on the north side of the Yumen River in Ximing Mine, China. We conducted slope restoration and environmental treatment of the CMW dump using shotcrete, deep hole grouting, external soil spray seeding technology with multiple soil fixation measures. The stability of the repaired CMW dump was analysed using LiZheng Slope Stability Analysis Software, and the displacement and stress of high-risk zones were assessed using MIDAS GTS NX Software. Our findings provide valuable insights for slope restoration and evaluation.

ABSTRACTRecovery of soil health offers insights into the mechanisms underpinning the stability of remediated ecosystems. Indirect assessment of enzymatic activity potential using substrate induced respiration (SIR), is commonly used to evaluate changes in soil microbial activity profiles, with their links to environmental and management factors. In this study, community level physiological profiling (CLPP) was employed to assess the impact of time on surface coal mine soil remediation. When compared with an adjoining control site, analysis of CLPP parameters offer useful insights and prospective tools for assessing soil health, following mining and remediation activities.

ABSTRACTThe stability of concealed goafs in open-pit mines under the influence of blasting disturbances is directly related to successful mining production. Based on large-scale similar simulations, this study steadily analyses the evolution of full-stress/strain and acoustic emission response information relevant to the instability and failure of the concealed goaf under multiple blasting disturbances in an open-pit iron mine. The results show that after three blasting disturbances, a tension-shear dislocation area is formed at the side corner of the goaf. The goaf roof’s vertical displacement exhibits a ‘two-stage increase’ before roof caving. In contrast, the axial stress on the side wall undergoes ‘two-stage pressure relief’ before roof caving. The stress pattern of ‘stretch-unload-compression-shear’ is observed near the free surface of the side angle before roof caving, while ‘two-stage pressurisation’ is observed at the far end of the free surface. This paper delivers a new way of exploring goaf stability under blasting disturbances.

ABSTRACTThere are many factors affecting the mechanical and geochemical stability of the surface paste disposal (SPD) method. The most important of these are the cracks occurring during the storage of tailings. The relationship between the volumetric water content (VWC), matrix absorption (MS), and oxygen (O2) values of the paste material are important in the formation of cracks. In this study, surface crack formations of the paste material stored at the laboratory in accordance with 3 different storage methods (cemented and uncemented) were observed during the pour and after the deposition, and crack intensity factor (CIF) was measured. The relationship between CIF with VWC, MS, and O2 parameters was examined. As a result, the CIF value decreases approximately by %75 with cement addition in the lowest and highest layers in the SPD method. In a case receiving the most rain, the crack intensity value of the highest layer was determined to be 3,9% in the Design 3 where cement was used in the lowest and highest layers. In addition, VWC, MS, and O2 levels were 35%, −29 kPa, and 17.5%, respectively, making the paste material more efficient in terms of mechanical and geochemical stability.

ABSTRACTPhytostabilization on sulfidic mine tailings is not yet commonly implemented in the field, making it a crucial area of focus for future reclamation efforts. This greenhouse study aimed to explore cost-effective amendments on metal(loid) immobilisation and identify grass species candidates for phytostabilization. This research found that (1) wheatgrass (Pascopyrum) and ryegrass (Lolium multiflorum) are suitable candidates for phytostabilization, and (2) it is cost-effective to reuse wastes of bauxite residue and compost to promote phytostabilization effectiveness. Compost should be carefully evaluated for phytostabilizing Zn in tailings since the Accumulation Factor and Translocation Factor of all selected species are large than 1.

ABSTRACTThis study aims to investigate the weakening of CO2-fly ash-based backfill (CFBF) material in cation water. Weakening is determined in an order of Na+ > Ca2+ > Mg2+ by slake durability and mechanical properties analysis. Na+ damages the microstructure of CFBF through the ion exchange phenomenon, causing the ejection of tetrahedral calcium. In Ca2+ and Mg2+ water, N-M-A-S-H gel is precipitated and filled in openings via a two electrical-layer mechanism, which mitigates the materials from further deterioration. On this basis, the suitable cation type and concentration range are proposed as criteria to implement CFBF in mine-water affected backfill coal mining.

ABSTRACTIn order to effectively manage the sludge in the settling ponds, it is necessary to understand the physical properties of the sludge according to the depth in the pond. A new sludge sampling device and method was proposed in this study. Sludge was sampled at three depths in four facilities in South Korea, and the water content, particle size, density, composition, and viscosity of the sediment according to the depth were measured and evaluated. These results can be used to predict the shape of sediment and the lifetime of the settling pond, and to plan and effectively implement sediment removal.

ABSTRACTThis research presents a new Simulated Annealing based stochastic optimisation algorithm to integrate geological uncertainty into the optimization process through multiple equiprobable simulated realisations of an orebody while considering stockpiling options and other relevant constraints. The stockpiling option is included, increasing the chances of processing high-grade and most certain ore blocks in early periods. The efficiency of the proposed algorithm in creating a single good enough production schedule that minimises the risk of deviation from production targets while maximising the net present value of the operation is demonstrated through three case studies, i.e. case A with 2448 blocks, B with 6,578 and C with 10,810 blocks. The comparison of results with the two-stage stochastic model reveals that the proposed methodology reduces the risk of production deviation to a minimal and provides a near-optimal solution with an optimality gap of 3.53, −0.87, and 8.19% for cases A, B, and C within a reasonable amount of time.

ABSTRACTSustainability is acknowledged as an emerging megatrend in business that significantly affects companies’ survival and competitiveness in the market-place. Environmental, global workforce and complex supply chain networks have created pressures to have a clear vision and improve sustainability due to geopolitical dimensions. As per United Nations (UN), Sustainable Development Goals (SDGs), responsible production and consumption (SDG 12), and industry, innovation and infrastructure (SDG 9), and attain higher economic scales of productivity (SDG 8) oblige to drive productivity improvement. The mining maintenance costs constitute around 30% to 40% of the direct mining costs in mining due to diverse operating conditions. First, this article aims to develop the Cox regression Machine Learning (ML) model to derive shovels’ Remaining Useful Life (RUL). Second, formulate and model the maintenance schedule optimisation of mining equipment. Third, test and validate the cost optimisation model by deploying Decision Optimisation (DO) ILOG CPLEX to combine maintenance schedules of Preventive Maintenance (PM) and Predictive Maintenance (PdM). Finally, the data-driven actions demonstrate operating cost reduction through the metrics of Overall Equipment Effectiveness (OEE), Overall Throughput Effectiveness (OTE) and Impact Factor (IF) computation. Further, this article demonstrates the benefits of IF improvements through a case study. The combined optimised maintenance reduced shovels’ maintenance by 2.27 hours per combined schedule, which led to the potential ‘OEE’: improvement between 2.7% and 7.2% of different shovels and which is a measure of equipment productivity. The computed IF improvement for mining shovels is 49% and is aligned as per the SDG of responsible production.

ABSTRACTThis paper examines the green energy transition objectives from a U.S. viewpoint. It is highly doubtful that targets announced by politicians, climate advocates and green investors will be achieved under-desired timeframes. The lack of a coherent domestic mineral policy, exceedingly long and burdensome permitting timeframes and increasing litigation will result in continuing underinvestment domestic mineral opportunities. As a result, geopolitical risks across necessary critical mineral supply chains are anticipated to escalate as the U.S. de facto strategy will be reliance on foreign sources, both friendly and not.

ABSTRACTAutomatic extraction of land use and land cover changes in mining areas is crucial for the supervision of mineral resources. This study proposes an automatic open-pit mine (OM) change detection method via a novel deep learning model, DA-UNet++ (Deformable-Attention-UNet++), and an object-based approach from high resolution remote sensing images. First, by embedding a deformable convolution module and attention mechanism module into UNet++ network, and constructing a skip sampling strategy, DA-UNet++ is proposed to combat the high heterogeneity of shape and scale of ground objects in OM environment and improve the change detection accuracy. Second, an object-based post-processing (OBPP) approach is presented for enhancing raw change detection results of DA-UNet++, which includes object-based expectation maximisation optimisation and morphological close operation. In the experiments, Season-varying Change Detection Dataset is first employed to evaluate the effectiveness of the proposed DA-UNet++ by comparing with other benchmark models. Then, self-made Ordos OM Change Detection Dataset including 38 OMs is utilised to verify the efficacy of the proposed method, and comprehensive metric F1−score of 85.6% is achieved. Evaluation results prove that the proposed method provides superior change detection performance compared with several benchmark methods, and has practical significance for OM management and monitoring.