Mine Planning Research Articles

A comparative study of hybrid adaptive neuro-fuzzy inference systems to predict the unconfined compressive strength of rocks

The accurate prediction of unconfined compressive strength (UCS) in rock samples is critical for the successful planning, design, and implementation of mining and civil engineering projects. UCS is crucial in assessing the stability and durability of rock masses, which directly influences the safety, efficiency, and cost-effectiveness of construction and excavation operations. Here’s a refined version of your text for enhanced clarity and flow: in this part, the execution of the proposed model was compared for both single and hybrid configurations. Hybrid models included support vector regression (SVR) combined with the Seahorse Optimizer (SVSH) and SVR combined with the COOT optimization algorithm (SVCO). For training, 70% of the UCS dataset was utilized, while the remaining 30% was equally divided between testing (15%) and validation (15%). For the model evaluation, several metrics were considered in this work, including the R2, RMSE, WAPE, MAE, and RAE, which ensure fairness in the analysis. The closer the R2 value comes to 1, the better the performance. The error metrics should be close to 0 for better accuracy. From Table 2, one can observe that the result of the standalone SVR model gave an RMSE of 6.213 during training and 9.454 during testing, hence showing poor performance. However, the inclusion of optimization algorithms significantly improved the performance of the SVR framework. Among the hybrid models, the SVSH model had the best performance, with an R2 value of 0.998 and an RMSE of 1.261 during training. The SVCO model performed moderately, with an R2 value of 0.988 during training.

Random Forest–Based Coal Mine Roof Displacement Prediction and Application

Coal mine roof accident is one of the most important geological disasters faced by coal mine, and roof displacement is an important index to measure the effect of roadway control and construction safety. Therefore, this study puts forward the machine learning method to study the advance prediction of coal roadway roof displacement. By identifying six important influencing indexes of coal roadway roof displacement, the prediction dataset of coal roadway roof displacement is established and the correlation and importance of the indexes are analyzed. Based on Random Forest (RF), XGBoost, and Gradient Boosting Decision Tree (GBDT) algorithms, three kinds of roof displacement prediction models are established respectively. R2, mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE) are selected to evaluate the performance of the models. The results show that the RF model has the best performance while the XGBoost model has the worst performance. When RF model is applied to coal mine roadway, the average R2 is 0.92, and the relative error of prediction is 1.76%–9.11%, which indicates that RF model has greater accuracy and applicability in advance prediction of coal roadway roof displacement. It can be used to predict the roof accidents of coal mines to ensure the safety and stability of the roadway enclosure rock during the construction period. The study will be helpful in planning of mining, improving the recovery rate of resources, and promoting the intelligent development of coal mine.

Cross-Hole Full-Waveform Inversion—A New Approach for Imaging Quartz Vein-Hosted Gold Deposits

To enhance the efficiency of mine planning, mining companies wish to understand the structure and extent of ore-bearing rocks as well as possible. Conventional seismic reflection surveys are not well suited for this purpose as they provide an image containing only the location of reflectors, and do not provide physical property information to discriminate between ore and gangue material. Full-waveform inversion (FWI) is a powerful inversion technique, which is able to recover the physical properties of the subsurface at a far greater spatial resolution and accuracy than conventional seismic methods. In this study, we synthetically examined the feasibility of using FWI to image quartz vein-hosted gold deposits. We utilised the Curraghinalt gold deposit in Northern Ireland to parameterise our models, where mineralisation is bound entirely to thin (1–3 m) and steeply dipping (>45°) quartz sulphide veins. Firstly, we demonstrated that a conventional surface seismic reflection survey geometry alongside FWI is infeasible for imaging quartz vein-hosted gold deposits. Secondly, we explored a cross-hole seismic survey geometry consisting of sources and receivers placed down vertical boreholes. This cross-hole survey geometry is capable of generating synthetic datasets such that FWI can recover the position of the veins in space accurate to within 0.5 m relative to their true positions, and recover their physical properties with an accuracy greater than 90%, beginning from an entirely homogeneous starting model. We conclude it is essential the source and receiver boreholes be positioned such that both transmitted and reflected arrivals are present in the datasets, otherwise FWI will fail to accurately recover the position and physical properties of the veins. This opens a new avenue for FWI to play a major role in the planning stages and development of gold mines around the world.

The implementation of information technology (IT) in the dynamic management of coal demand and supply processes

The article presents a methodological approach that encompasses the conceptualisation of a design idea and the implementation of an information technology (IT)-based solution to support models developed for energy mix management. It identifies the key elements that influence the definition and subsequent creation of an IT solution, focusing on processes that reflect both the demand (coal demand) and supply sides. In terms of the supply side, the IT solution will be equipped with planning information: Regarding minable resources, including general data on the resources currently held and the planned annual resources to be mined (qualitative data will not be included) The mining plans for each mine are accompanied by qualitative data (data that are standardised to ensure the quality of the production data collected. In addition to mining plans, the supply side will have up-to-date information on coal already mined. This information will be presented in quantitative and qualitative terms at various stages of the product life cycle, which begins with the mining of raw coal and ends with the formation of a finished product that can be sold and transported to the customer. The article indicates the key elements that must be precisely defined for the correct definition of processes on the demand side and the supply side, and in what order actions should be taken by IT specialists so that this solution fully satisfies the teams managing the processes dynamically.

An Open-Pit Mines Land Use Classification Method Based on Random Forest Using UAV: A Case Study of a Ceramic Clay Mine

Timely and accurate land use information in open-pit mines is essential for environmental monitoring, ecological restoration planning, and promoting sustainable progress in mining regions. This study used high-resolution unmanned aerial vehicle (UAV) imagery, combined with object-oriented methods, optimal segmentation algorithms, and machine learning algorithms, to develop an efficient and practical method for classifying land use in open-pit mines. First, six land use categories were identified: stope, restoration area, building, vegetation area, arterial road, and waters. To achieve optimal scale segmentation, an image segmentation quality evaluation index is developed, emphasizing both high intra-object homogeneity and high inter-object heterogeneity. Second, spectral, index, texture, and spatial features are identified through out-of-bag (OOB) error of random forest and recursive feature elimination (RFE) to create an optimal multi-feature fusion combination. Finally, the classification of open-pit mines was executed by leveraging the optimal feature combination, employing the random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers in a comparative analysis. The experimental results indicated that classification of appropriate scale image segmentation can extract more accurate land use information. Feature selection effectively reduces model redundancy and improves classification accuracy, with spectral features having the most significant effect. The RF algorithm outperformed SVM and KNN, demonstrating superior handling of high-dimensional feature combinations. It achieves the highest overall accuracy (OA) of 90.77%, with the lowest misclassification and omission errors and the highest classification accuracy. The disaggregated data facilitate effective monitoring of ecological changes in open-pit mining areas, support the development of mining plans, and help predict the quality and heterogeneity of raw clay in some areas.

Application of Electrical Resistivity Tomography for Improved Blast Movement Monitor Locations Planning

Blast-induced rock movement is a highly variable phenomenon which depends on numerous factors. Rock type and its deriving physical and mechanical properties are among such factors, which are crucial to be available prior to planning the locations where each monitor is placed. Hence, Electrical Resistivity Tomography (ERT) was used to acquire additional rock mass data for several benches of an open-pit mining operation. ERT is considered a valuable tool in mine planning. By providing information about the subsurface electrical resistivity distribution, ERT helps in determination of geological structures, composition of the ground and helps assessing potential hazards. A total of 21 profiles were measured, which allowed for relevant data to be obtained regarding each monitor’s location. Results from the study showed that blast movement measurements from different rock-type zones do not differ in a statistically significant manner. However, substantial differences were uncovered regarding the mode of movement vectors by using Kernel density estimation (KDE). These results proved that ERT is a fast and reliable support method for establishing different rock mass zones and for location planning of blast movement monitors and blast design optimization prior to drilling.

Research and Investigation of Factors Affecting the Behavior of Mine Pressure During Underground Coal Mining Operation

Coal is one of the essential energy sources, found abundantly within the earth's crust. Most coal mines worldwide, particularly in Afghanistan, are extracted using underground mining method. In order to extract coal mines through underground mining, well-reasoned and technical plans that answer all engineering and safety issues must be arranged. Extraction of coal layers underground causes various convulsions in the roof and floor of coal layers. The number of convulsions and its functioning depends on a series of different geological and technical factors. Therefore, in this research, the effective factors on how mine pressure works during underground coal mining have been researched and investigated using the library method, and later 16 effective factors have been selected for the occurrence of mine pressure. In order to determine the effect of these factors on how mine pressure works, a questionnaire was prepared and distributed to 32 university professors and professional engineers engaged in engineering in the mining sector. The results of these questionnaires show that all the selected factors have an important effect on the performance of mine pressure, including the type of roof rock, the direction of the sealing systems and the nature of the extraction pits have a great effect on the performance of the mine pressure during the underground extraction of coal mines.

GIS-based calculation method to predict mining subsidence in flat and inclined mining: A comparative case study

Prediction of ground movements in the case of continuous subsidence is critically important for the planning of underground mining. Many calculation models are used to predict mining subsidence. A comprehensive method to render current calculation models superfluous can only come from a theoretical model, but the challenge remains in defining the parameters, given the great variety of rock structures found. Hence, innovation through a conceptual and technological study of the subsidence mechanism is needed to ensure that this problem can be solved satisfactorily. In this study, a new method is proposed to predict ground surface subsidence by combining a stochastic medium concept with Geographic Information System (GIS) technology. All subsidence computations are implemented within GIS, where spatial components are used to conduct the subsidence prediction analysis. This paper includes simulations of basic subsidence phenomena and a comparative study of the GIS-based calculation method’s suitability against the empirical method from the Subsidence Engineer Handbook (SEH), semi-empirical influence function models, and numerical modeling. First, the influence of basic extraction area categories on the character of mining subsidence at the surface for flat seam layers is verified. Second, subsidence and horizontal displacement profiles are compared for both gently and steeply inclined mining. Finally, the verification of calculated horizontal strain values for an actual case of inclined irregular mining is also conducted. The comparative results of subsidence predictions for flat and gently sloping mining demonstrate the suitability of the GIS-based calculation method for use in underground mining strategy.

Visualizing and Quantifying Uncertainty in Cut-off Grade Selection

Modern mine planning techniques are advancing at an incredible rate, and mining companies are progressively more interested in quantifying the uncertainty in their business plans. However, for many decision makers and other stakeholders, the leap from tried-and-true deterministic techniques to stochastic methods will be challenging to understand and accept due to the complexity of the work involved. At the 2019 SME Annual Conference, the author introduced a multifaceted model to embrace uncertainty in mine planning (Roos 2019), and at APCOM 2023, the author brought forth some modern data visualization techniques that can be leveraged to achieve this goal (Roos 2023). The purpose of this model is to fill the gap in embracing uncertainty by recommending improvements in visualization and communication techniques to ensure that all mining operations can take steps toward embracing uncertainty quantification. This paper presents a case study leveraging this model to adapt the cut-off grade selection process for an underground gold mine to one that utilizes grade uncertainty and provides the appropriate decision makers with the information they need to embrace the uncertainty in their business plan. The methods presented in this paper do not produce a finite solution to the cut-off grade selection process, but instead provide additional information that can be used in understanding the impact of geological uncertainty on the results of a deterministic study. In this study, the visualization techniques presented here have been useful in identifying areas of the deposit with a higher risk of achieving the estimated grade and also areas where there may be an opportunity to develop future stopes with additional geological information.

Stope Dimension Analysis Based on Geomechanical Properties Using Brute Force Algorithm

Abstract Mining operations play a crucial role in meeting global resource demands, emphasizing the importance of determining optimal stope dimensions for safe and efficient ore extraction. However, the complexity arising from multiple geomechanical factors affecting stope stability poses challenges in identifying ideal dimensions. Traditionally, engineers use an iterative process for stability analysis, but its limited scope and time-consuming nature call for more efficient algorithms. This research proposes a series of algorithms based on the brute force method using Potvin’s empirical stability criterion. The algorithm efficiently generates and evaluates stope dimension scenarios, enabling comprehensive stability analysis. Testing on case studies demonstrates its remarkable speed and effectiveness, making it a valuable tool for mine planning and optimization.

Exploration of grade distribution in iron mines based on rough set extreme learning machine and multispectral

Ore grade is an important indicator for evaluating the mining value of a mine. Reasonable mining based on ore grade distribution can aid in sustainable development and grade control within mining areas. Therefore, this paper aims to utilize Sentinel-2 multispectral data and an extreme learning machine (ELM) to rapidly explore the grade distribution in iron ore mines. First, direct orthogonal signal correction and mathematical transforms are used to correct and enhance multispectral information. Then, we propose a rough set ELM (RS-ELM) to learn multispectral data for grade distribution exploration. Specifically, we first discretize the hidden layer matrix (H) of the ELM and calculate the dependency degrees of the nodes on each other according to the rough set method. Then, the nodes with high dependency degree are deleted to ensure the lightness of the network structure and reduce the complexity of the ELM. Furthermore, a whitening transformation is performed on H to improve numerical stability. Comparison experiments prove the effectiveness of the RS-ELM. Finally, the mine grade distribution exploration model is established by using Sentinel-2 multispectral data and RS-ELM, and the grade distribution map of the mine is drawn. The distribution map clearly shows the trend of grade distribution in the mine. Moreover, based on the cut-off and industrial grades, we categorize the mine area into three zones: non-ore, secondary ore, and main ore areas. This categorization provides a foundation for developing the mine’s mining plan and grade control.

Sustainability in Long-Term Surface Mine Planning: A Systematic Review of Operations Research Applications

The mining industry, critical for global resource supply, has historically been linked to significant environmental degradation and social challenges, including habitat disruption, water and soil contamination, as well as fatalities. But recently, the industry has been undergoing a transformative change to meet evolving environmental and societal expectations. Operations Research (OR) provides essential tools and techniques to optimize decision-making processes in this context. This paper presents a comprehensive review of different OR methods and their applications in balancing economic, social, and environmental objectives in surface mine planning. Of the 63 papers reviewed, 45 were published by researchers from three main countries: Canada, the United States, and Australia, with 73% of these papers appearing in journals. This review highlighted that linear programming (LP), dynamic programming (DP), Lagrangian relaxation (LR), stochastic programming, and metaheuristics are among the most widely applied methods in surface mine planning within a sustainable development (SD) context. By evaluating the strengths and limitations of these techniques, this review offers valuable insights for researchers and practitioners aiming to improve production efficiency and sustainability through advanced planning strategies.

A multi-objective constraint programming approach to address clustering problems in mine planning

PurposeThe mine sequencing problem is NP-hard. Therefore, simplifying it is necessary. One way to do this is to employ clusters as input instead of individual blocks. The mining cut clustering problem has been little addressed in the literature, and the solutions used are almost always heuristic. We solve the mining cut clustering problem, which is NP-hard, through single- and multi-objective optimization, finding results that are local optima in acceptable computational time.Design/methodology/approachWe first elaborate an ILP-based model to address the mining cut clustering problem. We employ a mono-objective approach and two multi-objective approaches, solving all these models by constraint programming. To choose the best solutions generated by multi-objective approaches, we employ two multi-criteria decision analysis approaches, considering different weight configurations. We developed a case study using real data.FindingsWe verified that the approaches based on multi-objective optimization performed better than the mono-objective approach for the economic return criterion. The weighted-sum multi-objective approach presented the best results considering all objective functions used. Once viable solutions were obtained through multi-objective optimization, multi-criteria decision analysis approaches almost always selected the same solution. We obtained solutions that are local optima in acceptable computational time.Research limitations/implicationsThis study solves an instance with 80 blocks. Consequently, it is aimed at short-term mine planning. The methodology has not yet been evaluated in large instances related to medium- and long-term mine planning.Originality/valueThis is the first time that multi-objective optimization has been employed to solve the mining cut custering problem. Even other problems related to mine planning were, at most, solved by goal programming, so that multi-objective optimization is a knowledge that is not widespread among mining researchers. The results are consistent, and the study achieves the objective of finding quality solutions to an NP-hard problem in an acceptable computational time.

Research on a coal seam modeling construction method based on improved kriging interpolation

To address the issues of large anomalous triangulations, invalid interpolations, and uneven boundary interpolations in kriging interpolation, we propose research on a coal seam modeling construction method based on improved kriging interpolation. The work methodology assumes that by introducing kriging interpolation and analyzing its problems, we improve the interpolation method via a local interpolation algorithm for large anomalous triangulations, an optimization algorithm for locally redundant interpolation points, and a nonuniform boundary adaptive local interpolation algorithm. These improvements allow the interpolation method to better reflect the variability and realistic nature of coal seams. The research results indicate that applying this method to the construction of the Dananhu No. 2 open-pit mine coal seam model has improved the issue of coal seam transition stiffness, such as abnormal large-area triangulation in areas with significant elevation differences. This approach appropriately reduces the memory space usage without altering the coal seam morphology (which saves approximately 27,000 KB of memory, equivalent to the space occupied by 4 out of 21 coal seams). It has also prevented inaccuracies in boundary line positioning and transitions caused by too low a density of points on the coal seam reserve boundary line, resulting in smoother model transitions at the boundaries that better align with the actual coal seam change trends, the error rate in coal quality estimation decreased by 62.69%. This study provides data support for mining planning and reduces costs. This method can be extended to the construction of all mine models.

Optimization of underground mining production layouts considering geological uncertainty using deep reinforcement learning

Mineral extraction plays a key role in the global raw materials supply chain, however the exhaustion of shallow deposits and typical scarcity of sampled data during exploration activities creates challenges in mine planning and design, where decision-making is highly sensitive to uncertainty in geology and mineral grade prediction. Geostatistical techniques are commonly used to generate a set of equiprobable simulated numerical models to capture these uncertainties, however incorporating these simulated models within a mine planning and design framework remains a major challenge. The purpose of this paper is to propose a novel approach to decision-making in underground mine design that can use information from an ensemble of numerical realizations of a mineral resource to improve the financial performance of the asset. A deep reinforcement learning (DRL) framework, using the proximal policy optimization (PPO) algorithm, is developed for the design of underground mining production level layouts. A case study is presented using a gold mineral resource characterized by an ensemble of 100 numerical realizations to verify the advantages of the proposed method, considering a baseline consisting of an industry standard automated design method. The DRL approach achieved an 8.3% higher expected profit, a 3.4% more gold mined than the baseline, and has the added functionality of considering uncertainty in mineral grades.

Integrating geometallurgy in the simulation of processing plant responses and applying policy gradient for shovel allocation in operating mining complexes

ABSTRACT Several short-term planning activities of mining complexes must be addressed simultaneously, such as extraction sequence, equipment allocation, digline definition, and destination decisions. Regarding stochastic mine planning, geological attributes and equipment performance uncertainties are more commonly addressed. However, processing response attributes at the ore treatment facilities also have uncertain components. This paper proposes a high-order simulation of processing plant responses given the blended ore attributes. First, it is included in the grade-control digline approach and later applied to a policy gradient-based framework that allocates shovels. A case study at a gold mining complex illustrates the benefits of the proposed approach.

Modeling and scenario building for climate change adaptation planning: The case of large mining in Chile

The mining sector in Chile is a strategic industrial sector for the country. Existing evidence shows that it faces several serious climate change threats; precipitation and flooding, droughts, heatwaves, among others. This scene put pressure on the government and the industry to develop efficient adaptation plans. To date, adaptation plans are designed under the rather linear paradigm of predict-then-act or the impact-lea approach as they are characterized in the IPCC Assessment Report 5 (AR5). Literature and the IPCC reports have identified the limits of those planning paradigms showing the relevance of adaptation barriers or enabling conditions, which should be considered as intrinsic part of the planning problem.In methodological terms, planning for climate change risks implies an enriched adaptation plan problem, previously characterized only by an operative climate risk management, that must be described at the beginning of the decision-making process. In this study, our objective is to contribute to the climate change adaptation planning of large-scale mining in Chile. The study is based on a climate change adaptation planning approach that overcomes the limitations of the current paradigm. In doing so, we start from the understanding that what emerges as an object of analysis from adding to the climate risks their enabling managing conditions is a social system. The social system whose function is that social climate risk management takes place. Therefore, we call it the Social Management System for Adaptation to Climate Change (SMSACC). As such the SMSACC should be the adaptation planning key object.In the first place we modeled that system applying a qualitative system methodology and then we developed it into a mathematical model based on graph theory, in particular the signed digraphs. This allows us to simulate two types of intervention on the enlarged object of analysis of the large mining adaptation plan. On the one hand, we carried out a future scenario analysis based on prospective tools which enables us to understand the system's answer to different future behavior of its environment, including climate change. On the other hand, we simulate different strategic interventions options on the system, which facilitates understanding the system's reaction under different public policy approaches.The modeling and simulation results provided an insightful understanding of the dilemma of social adaptation management of large mining in Chile, and as such they are useful input for the planning process.

Seismic imaging of deep‐seated gold deposit and host rocks through a reappraisal of legacy seismic data in the Fochville mining area, South Africa

AbstractReappraisal of legacy reflection seismic data has shown to deliver value in mineral exploration, particularly in brownfield settings. In this work, we demonstrate how the reappraisal and processing of legacy reflection seismic data can be advantageous in the mineral exploration industry. We use today's standard seismic processing tools to improve the imaging of deep and complex geological structures that host mineral deposits. The recovered and processed 25.3 km long legacy seismic profile in this study was acquired in 1983 by the Gold Division of Anglo‐American as part of the Witwatersrand Gold Fields exploration program. This study aims to improve the imaging of the Ventersdorp Contact Reef gold‐bearing horizon (termed reef), a world‐class gold deposit (2 m thick) situated at depths between ∼2400 and ∼4100 m below the ground surface near South Deep Gold Mine in Fochville, South Africa. The final processing results from the pre‐stack time and phase‐shift migration approaches clearly reveal a dipping reflection associated with the gold‐bearing horizon and major steeply dipping faults that crosscut and displace the deposit. The final results are integrated with borehole information, 1D synthetic modelling and aeromagnetic data to constrain the structural interpretation. In particular, 1D synthetic simulation and borehole data constrain the depth position of the gold deposit. The magnetic data provides additional constraints on the complex faulted blocks of the host rocks such as the intrusions that may have a direct impact on ore resources and evaluation. The mining companies, such as South Deep Gold Mine, operating closer to the seismic profiles can use this new structural information to update the current geological models and improve future mine planning and designs, thus providing some insight into the prospectivity of unmined ground.

Procedure Design and Reliability Analysis for Prediction of Surface Subsidence of a Metal Mine Induced by Block Caving Method—A Case Study of Pulang Copper Mine in China

Surface subsidence resulting from block caving mining causes considerable environmental and economic harm in mining areas, highlighting the critical need for accurate predictions of surface subsidence. Given the unique features of the block caving technique and the resemblance between the released ore pillars and the mining processes, this paper developed a lightweight model to forecast surface settlement utilizing the probability integration approach to address the issue of predicting surface settlement in metallic mines. This study focuses on the Pulang Copper Mine, situated in the northeast of Shangri-La County within the Yunnan Province, as a case example. This mine employs the block caving method, which results in substantial surface subsidence. A visual mining simulation program is designed to combine the ore mining plan with the prediction model, manage the ore output of each mining point in batches, treat the ore pillars released in the planning cycle as strip work, and simulate and calculate the surface area above the ore pillars settlement value. The calculated values of surface subsidence induced by ore drawing are then interpreted as the downward displacement of the surface subsidence beneath the strip workings. Furthermore, to verify the reliability of the model, three-dimensional laser point cloud data of the Pulang Copper Mine in recent years were collected, and the differences between the predicted surface and the measured surface were calculated and analyzed.

State-of-the-art optimization methods for short-term mine planning

Maintaining short-term planning aligned with the ultimate long-term plan is challenging. This requires many details to be modelled on a daily or weekly basis to reach this target. Short-term planning is more challenging than medium- and long-term planning because it deals with uncertainties, which increases the gap between medium- and long-term plans. Short-term mine planning teams are expected to identify and manage potential risks to mitigate them, and eventually achieve the long-term objective of maximizing the Net Present Value (NPV). Very few studies have identified the problems that exist in short-term mine planning and provided technical solutions to overcome them for open-pit mines. One of the complexities associated with short-term planning is the creation of polygons or mining cuts by clustering before optimizing and scheduling the plan to reduce the computational expense of mine planning models. The primary objective of this study is to review the latest papers describing short-term mine planning challenges and technical solutions proposed to optimize mine planning for open-pit mines.