Engineering

Abstract: This study investigates the effect of preliminary ultrasonic treatment on the flotation re-covery of copper from aged tailings of copper-porphyry ores. The initial sample was char-acterized by a low copper content of 0.17%, a high proportion of oxidized copper forms of 53.84%, and fine dissemination of copper-bearing minerals, which complicates their re-covery by conventional flotation. Ultrasonic treatment was applied after grinding as a physical method of surface activation. The pulp was treated at a power of 30 W and a fre-quency of 22 kHz, while the treatment duration varied from 0 to 25 min. The effects of ul-trasonic treatment on concentrate yield, copper grade, copper recovery, and enrichment efficiency were evaluated. One-way analysis of variance (ANOVA) was used to assess the statistical significance of the results, and SEM analysis was performed to examine changes in particle surface morphology. The best flotation performance was obtained after 15 min of preliminary ultrasonic treatment. Under these conditions, the copper grade in the con-centrate was 1.79 ± 0.06%, copper recovery was 44.37 ± 0.21%, and enrichment efficiency was 40.06 ± 0.38%. Longer treatment times did not improve the results. SEM observations indicated particle disaggregation and partial removal of slime coatings. The results con-firm that preliminary ultrasonic treatment can improve copper flotation from aged tailings.

Abstract: Elevated salinity can significantly affect the rheological behavior, thickening time, and early strength development of API Class G cement slurries. This study experimentally investigated the effect of NaCl additions of 0, 4, and 8 wt.% BWOC on cement slurries with densities of 1.5 and 1.9 g/cm³ at temperatures of 25, 50, 75, and 90 °C. Rheological parameters, the time required to reach a consistency of 30 Bc under atmospheric pressure, the times required to attain selected compressive strength thresholds, and the ultrasonic cement analyzer (UCA)-estimated compressive strength after 12 and 24 h were evaluated.The results demonstrated that the effect of NaCl was not pro-portional to its concentration but depended on slurry density, temperature, and the property being evaluated. NaCl generally reduced plastic viscosity and yield stress, whereas gel strength exhibited a non-uniform response with a pronounced dependence on temperature. Its influence on thickening time and early strength development was likewise non-monotonic. Under certain test conditions, higher NaCl concentrations accelerated the attainment of the initial strength thresholds; however, they did not consistently increase the UCA-estimated compressive strength after 24 h or improve all evaluated operational properties. Among the investigated salinity levels, the slurry containing 4 wt.% NaCl exhibited the most balanced overall performance in terms of rheological behavior, thickening time, and early mechanical properties across both slurry densities and the investigated temperature range. Nevertheless, this concentration did not provide the best performance for every individual parameter or under all test conditions. The findings provide a basis for the further op-timization of cement slurry formulations intended for use in high-salinity environments. Since the experimental program did not include independent repetitions, the observed differences should be interpreted as descriptive trends that require statistical validation in future studies.

Abstract: Extended-reach and highly deviated wells often adopt a double build-up trajectory. This profile contains two curvature transition zones: the build-to-tangent transition and the tangent-to-build transition. The annular flow undergoes severe restructuring in these zones. They are potential bottlenecks for cuttings transport. This work uses a 215.9 mm wellbore with 127.0 mm drill pipe as the reference case. Analytical expressions for the cuttings accumulation ratio in both transition zones are developed based on the three-layer transport model. For power-law drilling fluids, a generalized Reynolds number is introduced to reformulate the Dean number. A dynamic disturbance coefficient is constructed from the along-hole Dean number gradient. This coefficient captures the contrasting behavior of secondary flow. In one transition zone, the secondary flow decays. In the other, it suddenly emerges. The results show that when the two curvature radii are equal, the accumulation ratio in the tangent-to-build transition is roughly 1.15 times that in the build-to-tangent transition. Reducing the curvature radius from 250 m to 100 m increases the accumulation ratio by about 2.5 times. The ratio rises with inclination angle. The disturbance coefficient increases monotonically with build rate. After accounting for drill pipe eccentricity, the recommended minimum curvature radii are 160 m for the first build section and 220 m for the second. These findings offer a theoretical basis for trajectory design in sections with abrupt curvature changes.

Abstract: Designing interlevel pillars for deep, structurally complex vein deposits requires balancing rock mass stability against ore recovery. This study evaluates the geomechanical response of the Kenzhem area of the Akbakay gold deposit using a three-dimensional finite-element model that reproduces the lithological structure, irregular ore-body geometry, underground workings, and mined-out stopes. Four extraction scenarios were analyzed: complete extraction without pillars and mining with 10, 15, and 20 m inter-stope pillars. Stability was assessed using maximum total displacement, the extent and connectivity of yielded zones predicted by the generalized Hoek–Brown criterion, and numerical convergence. Pillarless extraction produced displacements of 2.6–3.0 m, a continuous yielded zone extending toward the surface, and loss of numerical convergence. A 10 m pillar reduced displacements to 0.50–0.66 m but did not provide a stable equilibrium state. Increasing the pillar thickness to 15 m reduced the maximum displacement to approximately 0.08 m, localized the yielded zones near excavation boundaries, and ensured convergence. A 20 m pillar produced only a minor additional reduction to 0.065–0.070 m without materially changing the failure pattern. Therefore, a 15 m inter-stope pillar provides the most rational balance between geomechanical safety and ore recovery under the investigated conditions.

Abstract: The transition from open pit to underground mining is becoming increasingly important as near-surface ore is depleted and operations seek to extend mine life. However, its environmental effects are still often discussed in separate technical areas rather than as one connected planning problem. This review examines the environmental impacts reported during the transition stage and proposes an integrated framework to support mine planning. Following a PRISMA-guided approach, peer-reviewed studies published between 2010 and 2026 were screened, and 68 studies were included in the final review. The findings show that the transition does not simply reduce environmental impacts; instead, it redistributes them across water, ground stability, air quality, energy use, land disturbance, and nearby communities. The main reported concerns include groundwater rebound and inflow, slope instability and subsidence, underground dust exposure, and higher ventilation-related energy demand, while some surface impacts, such as land clearing, dust, and blasting noise, may decline as open pit activity decreases. Overall, the transition should be treated as a distinct planning stage, and the proposed framework helps mine planners address these impacts together rather than separately.

Abstract: Mine drainage containing manganese (Mn) and zinc (Zn) poses a major environmental challenge. In Japan, Mn-rich mine drainage frequently exceeds the discharge standards of 10 mg/L for Mn and 2 mg/L for Zn. This study evaluated laboratory-scale continuous-flow constructed wetlands (CWs) for the treatment of both synthetic and actual mine drainage without external carbon addition. Two limestone-filled CWs (2 L), one planted with Phragmites australis and the other unplanted, were operated at hydraulic retention times of 1–2 d. The influent mine drainage contained approximately 66 mg/L Mn and 10–15 mg/L Zn together with Fe, Cu, Cd, and Pb. During 170 days of continuous operation, the planted CW consistently achieved lower effluent Mn and Zn concentrations than the unplanted CW under both synthetic and actual mine drainage conditions. Effluent Mn concentrations in the planted CW ranged from 5.5 to 20.0 mg/L, compared with 46.1–54.2 mg/L in the unplanted CW. Likewise, effluent Zn concentrations ranged from 0.21 to 3.0 mg/L in the planted CW and from 2.4 to 10.1 mg/L in the unplanted CW. The superior performance of the planted CW was associated with enhanced limestone dissolution, elevated pH, and favorable rhizosphere conditions that likely promoted biologically mediated Mn(II) oxidation and the formation of Mn oxides, while Mn carbonate precipitation may also have contributed to Mn removal. These findings demonstrate that limestone-based planted CWs can effectively treat Mn-rich mine drainage without external carbon addition and highlight their potential as sustainable, low-energy passive treatment systems for the long-term management of abandoned mine drainage.

Abstract: This study investigates the direct and indirect effects of mining activity on the Human Development Index (HDI) at the district level in Peru during 2009–2024. The research addresses whether mining activity contributes to local human development and whether its benefits extend beyond mining-producing districts through spatial spillover effects. Using a balanced panel dataset of 13,118 observations from 1,874 Peruvian districts, a spatial random-effects model based on a Spatial Durbin Model (SDM) specification is estimated. The empirical model incorporates the effects of mining activity, the State Density Index (IDE), and education, while controlling for spatial dependence among neighboring districts. The results provide strong evidence that mining activity has both local and spatial effects on human development. The direct effect of mining activity is positive and statistically significant (0.0231; p< 0.001)), indicating that districts with mining activity tend to achieve higher HDI levels. Furthermore, the indirect spatial effect is also positive and significant (0.0428; p=0.001), demonstrating that mining generates benefits that extend to neighboring districts through regional economic linkages, infrastructure development, labor markets, and public investment channels. The model also reveals a strong spatial dependence of human development (0.811; p< 0.001), confirming that district-level development outcomes are geographically interconnected. Additionally, state presence and education emerge as important determinants of human development. The State Density Index has a positive and significant effect on HDI (0.0418; p< 0.001)), while education presents the largest estimated effect ((0.2429; p< 0.001)). These findings indicate that mining activity contributes to human development, but its impact depends on the institutional capacity of the State and investments in human capital. The study concludes that mining should be understood as a territorial development factor whose benefits can spread beyond administrative boundaries, highlighting the importance of spatially coordinated public policies to transform natural resource revenues into sustainable improvements in human well-being.

Abstract: Flocculation-sedimentation is a widely used solid-liquid separation process in mineral processing and wastewater treatment, yet its optimization remains challenging due to nonlinear interactions among reagents, solids, and operating conditions. This study presents a statistically rigorous multi-response optimization framework combining HuberT robust regression, canonical analysis, and the Derringer desirability function, and demonstrates its application to coal slurry water treatment as a case study. Coal slurry concentration, polyacrylamide (PAM) dosage, and CaCl₂ concentration were used as factors, with supernatant turbidity and initial settling velocity as responses. A central composite design (20 runs) was employed, and second-order models were fitted via HuberT M-estimation to mitigate the influence of potential outliers. Both the ln-transformed turbidity model (R² = 0.9887) and settling velocity model (R² = 0.9908) showed high significance and predictive capability. Canonical analysis confirmed that the turbidity response surface exhibits a true minimum within the design space, while the settling velocity surface has a saddle-point structure. HuberT weight diagnostics identified no downweighted runs for turbidity and two mildly influential runs for settling velocity, confirming overall data consistency. Multi-response optimization via the Derringer desirability function yielded a combined optimum (coal slurry 25.27 g/L, PAM 5.19 mg/L, CaCl₂ 2.07 g/L; desirability D = 0.8981) with predicted turbidity of 29.21 NTU and settling velocity of 13.26 mm/s. The proposed framework is generalizable to other flocculation-sedimentation systems requiring simultaneous improvement of multiple, often conflicting, process responses.

Abstract: Poorly governed geoscience databases introduce subtle but systematic biases that propagate through mineral resource estimation workflows, distorting grade continuity and resource classification. While the importance of data quality in mining is widely acknowledged, quantitative demonstrations of how specific data-management decisions translate into downstream technical and economic impacts remain limited. This study addresses that gap through three case studies derived from real-world industry examples. The first case study quantifies operator-dependent data extraction bias by comparing two independently generated datasets sourced from the same database on the same day. Despite nominal equivalence, the datasets differed materially in record counts and grade distributions, producing only negligible global volumetric differences (~0.1%) but up to ~5% local geometric variability and a 19.5% difference in estimated copper grade, resulting in a ~36% divergence in projected revenue. The second case study evaluates analytical uncertainty near detection limits using duplicate assay pairs, demonstrating that relative error increases markedly at low concentrations and that this behavior reflects inherent analytical limitations rather than laboratory non-compliance. The third case study examines the common practice of assigning detection-limit placeholder values to un-assayed intervals, showing that such substitutions can artificially generate ore in barren domains, whereas retaining null values and applying assignments during post-processing yields geologically and statistically coherent results. Collectively, these case studies demonstrate that hidden data biases can exert a stronger influence on resource outcomes than estimation methodology alone. The results highlight the need for standardized extraction workflows, explicit treatment of low-grade uncertainty, and validation practices that extend beyond global reconciliation metrics. Robust data governance and transparent, reproducible workflows are essential to reducing compounding uncertainty and improving confidence in mineral resource models. All datasets have been anonymized, scaled, or modified to prevent identification of any specific project, company, or operation. No confidential or propriety datasets are disclosed.

Abstract: Downhole vibrations are a primary cause of premature wear and failure of rock-cutting elements (RCEs) during well drilling, yet existing models often treat axial and torsional vibrations separately and neglect the feedback between wear flat growth and dynamic loading. This study develops an integrated finite element model that couples all three vibration modes (axial, torsional, lateral) with the evolution of the wear flat, friction, and temperature. The model is validated against laboratory experiments on a drilling stand using MEMS accelerometers. Two types of tungsten-cobalt (WC-Co) inserts were compared: uncoated and coated with a 3–5 nm titanium nitride (TiN) layer. Thirty tests were performed on granite and sandstone under varying weight on bit (WOB = 1.0–2.2 kN single-RCE) and rotation speed (RPM = 80–120). The TiN coating reduced axial RMS acceleration by 18%, torsional amplitude by 24%, and wear rate by 27% (from 0.154 to 0.112 mm/h) in granite. Frequency spectra revealed a resonant torsional peak at 55 Hz when RPM exceeded 120, with torque fluctuations increasing by 240%. A safe operating chart was constructed, defining green (WOB 1.0–1.6 kN, RPM 80–110), yellow, and red zones. The recommended regime (WOB = 1.7 kN, RPM = 105) gives 94% of maximum rate of penetration while reducing predicted wear by 35% compared to the red zone. The model prediction errors are 8–12% for axial and 10–15% for torsional vibrations. This work demonstrates that nanoscale TiN coatings and low-cost MEMS sensors can substantially improve vibration control and wear resistance, providing practical operating charts for drilling optimization.

Abstract: Quarry waste piles are heterogeneous engineered embankments that are susceptible to slope instability, yet early detection of pre-failure surface changes remains challenging due to complex surface conditions and measurement uncertainty. This study presents an integrated remote sensing–based framework for monitoring quarry waste pile instability by combining multi-temporal change detection with scale-dependent surface roughness analysis. Multi-epoch UAV-mounted LiDAR and photogrammetric point clouds were acquired before and after documented failure events at multiple active quarry sites. A standardized workflow was implemented, including precision alignment using a Recursive Iterative Closest Point (R-ICP) registration strategy, vegetation filtering with a multiscale CANUPO classifier, and uncertainty quantification through a Level of Detection (LoD) analysis. The resulting LoD thresholds were 10–15 cm for LiDAR-to-LiDAR comparisons and 34–36 cm for mixed-sensor datasets. Multi-scale roughness analysis revealed that zones which later experienced instability exhibited consistently higher and more heterogeneous roughness than adjacent stable areas within a well-defined linear scale range. A roughness-based A/D indicator enabled objective delineation of hazardous zones prior to failure. Post-failure monitoring showed surface smoothing following major displacement, followed by renewed roughness increases associated with secondary movements. These results demonstrate that scale-dependent roughness provides complementary information to displacement-based change detection and supports proactive geohazard monitoring of quarry waste piles.

Abstract: Variations in goaf temperature and CO concentration during the low-temperature oxidation stage are affected by residual-coal oxidation, air-leakage oxygen supply, gas generation, migration and dilution, and ventilation disturbance, leading to multi-source coupling and nonlinear response characteristics. To continuously characterize this process, this study proposes a joint prediction method for goaf temperature and CO concentration based on multi-source monitoring feature fusion and Covariance-Adaptive Whale Optimization Algorithm (CA-WOA)-optimized models. Field monitoring data, including goaf-pipe gas, working-face-side gas, upper-corner gas, and return-air-side gas, were integrated to construct a daily-scale multi-source monitoring dataset. Pearson correlation analysis and random forest-based feature importance ranking were combined to identify dominant variables and construct top-k feature subsets. CA-WOA was then developed from the original Whale Optimization Algorithm by incorporating rank-weighted elite-center reconstruction, covariance-adaptive direction learning, WOA random-search injection, and geometric step-size decay. A composite normalized error of temperature and CO concentration was used as the fitness function to optimize the hyperparameters of RF, XGBoost, LightGBM, CatBoost, LSSVM, and TABM under a unified evaluation criterion. The results show that CA-WOA improved the overall predictive performance of all six models. Compared with the corresponding unoptimized baselines, the maximum increase in Mean R² was 0.076, and the maximum reduction in the composite fitness F was 22.8%. CA-WOA-TABM achieved the best performance, with a five-fold average Mean R² of 0.924 and F of 0.273. Its out-of-fold R² values for temperature and CO concentration were 0.928 and 0.931, respectively, demonstrating stable internal validation performance. SHAP and PDP analyses identified GoafPipe_CH4, GoafPipe_CO2, GoafPipe_C2H6, and GoafPipe_O2 as key variables, indicating nonlinear response relationships associated with gas generation, oxygen supply, and gas migration in the goaf during continuous monitoring.

Abstract: In open-pit optimization software, metallurgical recovery is commonly treated as a constant for every block, although it varies with ore type and grade. Here, recovery is modelled as a block-grade-dependent variable using 24 laboratory flotation tests on the sulfide ore of a copper deposit in eastern Türkiye, and its effect on net present value (NPV) and the cut-off grade decision is examined. The deposit is split by sulfur content into two routes: sulfide ore (S ≥ 9%) to flotation and oxide ore (S < 9%) to heap leaching. Across a feed grade of 0.22–6.99% Cu, the measured recovery increases with feed grade from about 61% to 94%; the linear correlation is only moderate (r = 0.60), but the relationship is well described by a bounded, saturating recovery–grade curve (R(g) = R_max•g/(g + k); R_max = 0.95, k = 0.12; R² = 0.87). For the leach route, where no test data are available, a fixed 80% recovery is retained throughout. Optimization I (fixed recovery) and Optimization II (variable recovery on the sulfide route) are compared over ten scenarios, using a slope-constrained ultimate pit (50° overall slope, 5% discount rate). Because the deposit's copper is concentrated in high-grade sulfide blocks with measured recovery of about 90–93%, the metal-weighted recovery of the sulfide ore is 88.9%, and the fixed 80% assumption underestimates both recoverable copper and NPV. Under a fixed pit and cut-off, variable recovery yields roughly 8% higher NPV and about 2.9 kt more copper. When the cut-off grade is instead determined economically, variable recovery reclassifies the marginal low-grade sulfide ore (~0.05 Mt, measured recovery below 80%) as uneconomic; even so, total copper output rises from 40.2 kt (fixed) to 43.0 kt (variable) — a slightly smaller gain, because this marginal ore is excluded. Grade-dependent recovery derived from laboratory data thus determines both the recoverable metal and the marginal-ore boundary more realistically than a fixed assumption, and materially affects NPV for this deposit.

Abstract: Acoustic Emission (AE) measurements have many uses to evaluate the integrity of mate-rials. AE is often used to detecting leakage in pipelines. It has also been used to monitor changes in strength properties of fiber reinforced concrete. In the oil and gas industry, AE is predominantly used to study fracture initiation and propagation. In particular, charac-terization of samples is key for evaluating subsurface formations for successful under-ground storage. Research has been done to understand the behavior of AE in uniaxial compression and single stage triaxial compression tests. However, the validity of this method has not been documented in a multistage triaxial test. This characterization is required to understand the stability of the host rock under the related stress changes and potential mineralogic changes which may occur. Typically, there is a shortage of geolog-ic samples. A single multistage triaxial test eliminates the need for twin samples and provides an economic and time saving protocol compared to conventional methods. A single multi-stage triaxial (MST) test allows a constitutive model to be developed for a host rock. This work establishes a protocol for performing these tests with minimal correc-tions to the measurements. Acoustic Emissions were measured on five different samples undergoing Multi-stage Triaxial Tests. Two different behaviors were observed. For the “coarse grained” samples, designated Group 1 (Miocene sandstone, Wilcox and Cambri-an sandstone), the number of AE events did not show a strong dependence on confining stress. They did show an exponential increase of AE events with increasing deviatoric stress during each stage. In contrast, the Group 2 samples (Niobrara Marl and Niobrara Chalk) exhibited a significantly different stress dependent AE behavior. The amplitude of the AE events is significantly smaller than the quartz dominated samples indicating a more ductile and diffuse failure mechanism. The correction between maximum compres-sive strength and the point of positive dilatancy is still 1.2 for these samples, even though a different pattern of AE events is observed.

Abstract: This study examines the impact of regrinding on the interfacial properties of sulfide minerals and the flotation performance of weathered copper-porphyry tailings. The feed material is characterized by a low copper grade (0.17%) and a high proportion of oxidized species (53.84%), which contributes to its inherent chemical stability and poor flotation kinetics. The findings indicate that regrinding serves a dual role: facilitating the liberation of mineral intergrowths and inducing mechanical surface renewal. This renewal is characterized by a significant decrease in the oxidation-reduction potential (ORP) and an intensification of the surface reactivity. Experimental results identify an optimal grinding fineness of 77-81% passing -0.045 mm, yielding a copper recovery of 16.26% in the absence of a sulfidizing agent. The integration of sodium sulfide (400 g/t) with regrinding significantly enhances recovery to 36.37%, driven by the establishment of a reducing environment (ORP ≈ -150 mV) and the chemisorption-mediated activation of mineral surfaces. While ultrafine grinding (90-100% passing -0.045 mm) further increases recovery to 51.47%, it is accompanied by deleterious sliming effects and a subsequent loss of process selectivity. The study confirms that mechanical surface rejuvenation and the optimization of electrochemical conditions are critical for improving the processing efficiency of anthropogenic resources. providing a theoretical framework for establishing rational beneficiation regimes.

Abstract: The loess gully region is characterized by complex terrain with crisscrossing gullies,where coal mining can readily induce surface subsidence and slope deformation. Such deformation often leads to geological hazards and ecological issues,including collapses,landslides, soil erosion, vegetation dry up,and land degradation.Therefore,understanding the deformation behavior of mining‑induced slopes is essential for the restoration and management of mine geological environments.This study focuses on five slopes within working faces 50205 and 50206 of the Zhen’er Coal Mine in Fugu County.Using a combination of 3DEC numerical simulations and orthophoto-based fracture identification, we systematically investigated mining-induced slope deformation under the complex topographic conditions of the loess gully region.The goal is to answer three key questions: where mining-induced slope deformation primarily occurs,how it evolves over time, and what the main controlling factors are.Spatially,the primary deformation zones and their propagation paths vary significantly among the five slopes.The largest deformation occurs in the slope body directly above the main section of the working face,gradually decreasing toward the edges of the working face. Temporally, mining-induced slope deformation exhibits a time lag, meaning that surface responses lag behind underground mining activities and continue to develop even after the working face is fully extracted.In the loess gully region, slope deformation induced by mining is controlled not only by mining activities but also by topographic factors such as slope shape, aspect,gradient, and height. The spatiotemporal evolution of deformation becomes even more complex for slopes that span multiple working faces. These findings provide a scientific basis for monitoring mining-induced slope deformation and preventing geological disasters in the loess gully region,while also offering practical guidance for safe mining operations and hazard control in similar settings.

Abstract: Autonomous Haulage Systems (AHS) have significantly transformed surface mining operations by improving safety, productivity, and operational consistency. Currently, implementations predominantly rely on vehicle-centric perception architectures. Onboard LiDAR, radar, cameras, and Global Navigation Satellite Systems (GNSS) perform sensing, interpretation, and decision-making in individual systems. Decision-making is done using onboard LiDAR, radar, and cameras, and Global Navigation Satellite Systems (GNSS) in individual systems. These approaches enable collision avoidance and path tracking. They remain limited in their ability to account for the broader, dynamic mining environment characterized by dust, terrain degradation, geotechnical instability, heterogeneous traffic, and rapidly evolving operational conditions. This paper presents a systematic review of dynamic vision systems deployed in surface mining. It critically analyses the transition from solitary vehicle autonomy to interconnected, ecosystem-aware intelligence. The review synthesizes literature from mining automation, robotics, intelligent transportation systems, and multi-agent perception. This is to assess sensing technologies, perception algorithms, sensor fusion strategies, and environmental robustness techniques. Attention is given to the limitations of ego-centric perception models in complex open-pit ecosystems. Building on identified gaps, the paper proposes a conceptual framework for Ecosystem-Centric Dynamic Vision (ECDV). This perception is augmented through integration with fleet communication networks, dispatch systems, digital twins, geotechnical monitoring platforms, and environmental sensing infrastructure. The framework outlines a multi-layer architecture enabling cooperative perception, predictive hazard modeling, and risk-aware decision support at the mine-wide level. The review concludes by defining a research agenda for transitioning from vehicle autonomy to ecosystem intelligence in surface mining. It highlights opportunities in cooperative perception, adaptive sensor fusion under degraded visibility, and digital twin integrated predictive safety systems.

Abstract: Autonomous Haulage Systems (AHS) have become increasingly important as mining operations seek to improve productivity and remove workers from hazardous environments. The systematic integration of this technology requires not only operational change management but also a deeper understanding of mine-planning implications. Existing literature describes AHS and implementation guidelines with a focus on operational safety and autonomous system architecture, but it does not systematically address required planning-level adaptations. This study aims to identify how surface mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, with emphasis on identifying the principal aspects of AHS that must be considered in mine-planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, and show that road-width and load-channelization questions remain site-specific research needs rather than settled design rules. The study highlights the need for (i) mine-planning frameworks that treat AHS as a constraint on pit geometry, haul-road structural and functional design, fleet selection, production scheduling, roadmaintenance strategy, economic and social evaluation; (ii) human-systems integration and improved human-autonomous collaboration; and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

Abstract: Natural lateral particle segregation commonly occurs during the hydraulic deposition of slurry and thickened tailings in surface tailings storage facilities (TSFs), producing spatial heterogeneity in physical, hydrogeotechnical, and mineralogical properties, as well as in the water table. In sulfide-rich tailings, such heterogeneity complicates the design of reclamation cover systems, which are themselves affected by it. This study investigates the impact of physical and rheological properties of hard-rock mine tailings slurries on their segregation under hydrodynamic conditions. It proposes a multiparametric equation for the segregation index (SI) based on Buckingham's π-theorem. For this purpose, six flume experiments were conducted using tailings with initial solids mass concentrations of 63%, 66%, and 69% at slopes of 0.5% and 1%. Results revealed strong exponential correlations (R² > 0.95) between SI and tailings' physical properties (solids concentration, bulk density) as well as rheological parameters (Herschel–Bulkley yield stress and flow index, Cross infinite dynamic viscosity). The SI equation was developed using MATLAB nonlinear least-squares optimization with a trust-region reflective algorithm. Using an SI threshold of 0.05 to define non-segregating behavior, the proposed model can predict segregation tendencies as a function of tailings properties and slope conditions. Further laboratory and field investigations are needed to validate and generalize the criterion.

Abstract: Turbulent drag reduction (DR) using polymers is a critical technique for energy conserva-tion in fluid transport systems. Traditional monitoring methods relying on pressure transducers are intrusive and lack real-time turbulence characterization. This study pro-poses a novel non-intrusive intelligent monitoring system based on Fiber Bragg Grating (FBG) sensing and Artificial Intelligence (AI). An experimental setup was constructed to investigate the DR performance of polymer solutions. FBG sensors were utilized to capture the optical spectrum shift induced by turbulent flow fluctuations. A deep learning model was trained to correlate the optical signal features with the drag reduction rate. Results demonstrated that the AI model achieved high prediction accuracy (R² > 0.95), effectively replacing complex fluid dynamic calculations with optical signal analysis. This work provides a promising approach for real-time, non-intrusive monitoring of fluid flow characteristics in industrial applications.