Mine Scheduling Research Articles

Perancangan Perancangan Dan Penjadwalan Penambangan Tahun 2023 Di Pit 3 PT. Mutiara Fortuna Raya 180 Kabupaten Muaro Jambi

PT Mutiara Fortuna Raya's mining operations are currently guided by an annual mining design without detailed mining scheduling. However, variations in mining direction and sequence can lead to discrepancies in coal expose achievement, ultimately impacting coal production outcomes. High SR locations demand a more overburden volume and time. This research aims to evaluate various mining direction options to determine the most optimal approach. The research methodology uses a quantitative approach, comparing three mining scheduling scenarios: panel scenario, strip scenario, and block scenario. These scenarios are based on the 2023 reserve design results of 180,518 tons coal and 385,390 BCM of overburden. The outcomes of these three scenarios reveal varying stripping ratios across each period. Stripping ratio for the panel scenario: Quarter I SR 10.84, Quarter II SR 4.77, Quarter III SR 1.64, and Quarter IV SR 0.20. Stripping ratio for the strip scenario: Quarter I SR 5.11, Quarter II SR 2.40, Quarter III SR 1.67, and Quarter IV SR 0.53. Stripping ratio for the block scenario: Quarter I SR 8.93, Quarter II SR 3.10, Quarter III SR 1.74, and Quarter IV SR 0.21. Analysis of three scenarios indicates that block scenario presents the most favorable schedulling for implementation.

Economic evaluation of mineral deposit: A bottom-up approach

Proper economic evaluation of a mineral deposit is critical to effective investment decision-making in a mineral project. However, this often requires detailed mine production scheduling to produce a schedule that reflects the actual cash flow when the project comes on stream. Because of the dexterity required for this task many mine planners and explorationist attempt to use statistical formulas that approximate the mine scheduling and value of the project. The mathematical model developed for production scheduling often produces a constant production rate schedule over an approximate life span of the project. In this paper, we have attempted to apply a bottom-up approach that begins with geometrical modelling and equipment deployment pattern to define the number of equipment required for each sequence of operation based on available workfront in the development of each bench. Then, based on the number of equipment and the production rate at each sequence of operations, a production schedule is developed. This production schedule therefore will reflect annual cash flow since it is based on the sequence of operation. Application of this method on the west pit of Itakpe mine shows a considerable net present value and internal rate of return of the deposit compared with the evaluation made using statistical models. The NPV of the west pit was found to be USD621 million as against USD122.41 million using Nwosu’s formula and USD123.85 million Taylor’s formula. The above value of NPV using the proposed method shows the maximum expected NPV of the mineral project-based technical restrictions. An understanding of this value can guide the mineral property owner in decision-making.

New Method to Study the Effectiveness of Mining Equipment: A Case Study of Surface Drilling Rigs

The sustainable development of mining processes requires a deep knowledge of the effectiveness of mining equipment and is quite complex to analyze due to the intrinsic characteristics of the mining industry. In this regard, its measurement and control can lead to appropriate management, improving the mining processes’ efficiency, increasing safety levels, and reducing environmental impacts. This paper developed a new methodology to study the effectiveness of equipment in mining processes, researching the impacts of process management, process control, operating conditions, operational mining schedule, and maintenance programs on the effectiveness of drilling rig fleets using two actual coal mines located in the northwest of Spain and data collected over 10 years. Thus, a new method, called Overall Mining Equipment Effectiveness (OMEE), was developed, verifying its appropriateness and flexibility to analyze the effectiveness of mining equipment by examining the availability rate, utilization rate, and productivity index.

Analysis and Research on Mine Safety and Management Methods in the Context of Big Data

Abstract The optimization of mine safety and management methods is critical in the context of big data. This study will explore improving mine safety and management efficiency by utilizing CPS. A mine data interaction mechanism is constructed using CPS technology to realize efficient data collection and processing. The study profoundly explores the data interaction of end devices and the value model of computing resources by analyzing the mine CPS perceptual execution layer. The results show that CPS technology can significantly improve the efficiency of mine safety management. For example, the introduced CRV incentive mechanism significantly improves the utilization efficiency of computing resources. In addition, this study also explores the scheduling strategy of computing resources under multi-objective constraints and the implementation of mine scheduling based on the MOCA-PSO algorithm, which effectively optimizes resource allocation and utilization. The conclusion shows that applying big data technology and CPS can effectively improve mine safety and management, which is of great significance in guiding the future development of the mining industry.

Study of the impact of the open pit productivity on the economic indicators of mining development

The article studied the issue of improving the scientific and methodological base in the field of design and planning of open-pit mining by developing new and adjusting existing methods for determining the ore productivity of an open-pit, which should take into account the interconnection between the mining schedule and the productivity of an open-pit for ore, based on the condition for ensuring the standard volume of finished to the extraction of reserves. When choosing the production of an open-pit for ore, it is necessary to take into account the impact of the interconnection between the schedule of mining and production, based on the conditions of regulatory regulation of the volume of mining for the excavation of reserves, on the cost of ore mining and concentrate production. With an increase in ore productivity, the cost of ore mining, as well as the cost of production of concentrate reduction. At the same time, the very possible productivity of the open-pit for especially minerals is achieved, the economic efficiency of the development of deposits is achieved with the chosen direction of the open-pit. A decrease in productivity for a common mineral by 20 and 40% entails a decrease in profit from the development of the deposit by 10 and 20%, respectively. It has been determined that an increase in the slope angle of the working side of the open-pit significantly reduces the economic efficiency of the development of the deposit due to the fact that the operation of the open -pit with a large slope angle entails not only a decrease in the current stripping ratios as a result of an improvement in the mining schedule, but also a decrease in ore productivity, which reduces the present value of marketable products due to an increase in the period of development of the deposit. The results of the studies performed can be used by design organizations and mining enterprises in determining the ore productivity of an open-pit.

A Deviation-Minimization Approach to Short-Term Underground Mine Schedule Optimization

A Deviation-Minimization Approach to Short-Term Underground Mine Schedule Optimization

A review of open pit mine and waste dump schedule planning

In seeking to maximise NPV (Net Present Value), open pit mining schedules depend on optimal sequencing of pit progress along with ore-waste segregation. Although extensively researched using mathematical models and linear programs dating from the late 1960s, this subject continues to evolve with the emergence of innovative approaches and new computational techniques. Whereas past models have focused almost exclusively on ore extraction as the principal cost-intensity factor, waste movement and handling are less well understood. However, as it comprises some of the largest operational and reclamation expenses, waste management has been progressively incorporated into the mine scheduling process, with dedicated studies commencing from 2007. This paper focuses on the impact of waste movement and placement on pit schedule optimisation, with strategic plans that honour both cost and environmental constraints.

A Case Study of Incorporating Variable Recovery and Specific Energy in Long-Term Open Pit Mining

Integrated Optimization can find optimized solutions for a project to define open pit and mine scheduling with greater reliability. This work aims to demonstrate how the insertion of geometallurgical variables can significantly change the financial return of a project. Two geometallurgical variables are considered in mine planning simulations. Specific energy corresponds to the energy consumed in the comminution of the ore, and process recovery measures the percentage of metal incorporated into the product. Three scenarios were developed considering an iron ore deposit. In the Base Case (BC) scenario, the recovery was fixed, and the specific energy of comminution was not considered. GeoMet1 considers the variable recovery varying for each block. GeoMet2 considered both recovery and specific energy as variables varying for each block. GeoMet1 and GeoMet2 presented Net Present Value (NPV), respectively, as 3.68% and 13.57% lower than the BC. This overestimation of the BC results can be viewed as an optimistic case of mine planning that is very common in the mining industry. These results show that the use of specific energy and recovery variables is fundamental to obtaining more reliable mine planning.

Data-Driven Synthesis of a Geometallurgical Model for a Copper Deposit

Geometallurgy integrates aspects of geology, metallurgy, and mine planning in order to improve decision making in mining schedules. A geometallurgical model is a 3D space that is typically synthesized from early-stage small-scale samples and is composed of several metallurgical units, or domains. This work explores the synthesis of a geometallurgical model for a copper deposit using a purely data-driven unsupervised approach. To this end, a dataset of 1112 drill samples is used, which are clustered using different methods, namely, k-means, hierarchical clustering (AGG), self-organizing maps (SOM), and DBSCAN. Two cluster validity indices (Silhouette and Calinski–Harabasz) are used to select the final model. To validate the potential of the proposed approach, a simulated economic evaluation is conducted. Results demonstrate that k-means exhibits a better performance in terms of modeling and that using the obtained geometallurgical model for mining scheduling increases the project’s Net Present Value (NPV) by as much as 4%. Based on these results, the proposed methodology is an appealing alternative for generating geometallurgical models within greenfield, brownfield and ongoing operations.

Development Mine Scheduling Concept in Making Pit Design

Planning a modern open pit mine requires a computer model of the resources and reserves to be mined, both block and grid models. With current technological developments and has entered almost all sectors including the mining sector, as for computer program packages (software) used by the mining industry, namely Microsoft excel, Mincom minescap and others. An evaluation and analysis system of mine plans/ mine schedules is necessary to make predictions of mining operations that will be carried out and can be used to find the cause of deviation or design deviations. There are several weaknesses in making scheduling manually, so scheduling is done using SPRY which is very helpful in the scheduling process.

Technology of an open pit refinement under limit stability of sides

Purpose. To establish the feasibility of refining deep open-pit mines below the boundary of the use of combined motor-conveyor transport with an increased slope angles of the pit walls using the developed transport unit for reloading rocks to overlying horizons during the reactivation of pillars under transport berms. Methodology. Preparation of a digital block model of the deposit, the elaboration of 3D geomechanical models for the dynamics of mining, 2D and 3D numerical simulation of the rock stress-strain state of the outcrops of opencast workings, mathematical modeling of stepwise ore reserves and mining schedule, patent research and feasibility study. Findings. It is advisable to carry out mining in terms of the marginal rock state with an increase in the slope of the pit sides below the limit of application of the cyclic and continuous method in ultra-deep open pits. Such design of pit sides is achieved when benches are mined from top to bottom within the boundaries of steeply inclined layers with the use of inter-bench loaders of the developed designed in the completion zone. Provisions for the selection and feasibility of using the loader in the deep zone are formulated based on demarcation of application zones of cyclic (road transport) and cyclic-flow (combined road-conveyor transport) technologies. Originality. Schematization of the mining operation was performed based on the calculated values of safety factor of sides, which allows increasing the slope angles of the pit walls of even ultra-deep open pits in the completion zone. It was found that with deepening of mining, the zones of potential sliding move away from the loose overburden to lower ore benches closer to the final depth of the Kacharsky open pit (760 m), but the safety factor corresponds to the required value according to the design standards. Practical value. An increase in the slope of the pit walls in the completion zone can be achieved using the developed loading installation, the main difference of which is that it can be moved without dismantling under conditions of reactivation of transport pillars (with an increase in lifting height by 1.54.5 times compared to the known equipment).

An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models

Deep learning (DL), a subset of machine learning (ML) has been a popular research interest after obtaining remarkable achievements on various tasks like image classification, object detection, language processing, and artificial intelligence. However, the successes of these algorithms were highly dependent on human expertise for hyperparameter optimisation and data preparation. As a result, widespread application of DL systems in minerals processing is still absent despite the increasing ability to collect data from process information (PI) and assay data. Automated Machine Learning (AutoML) is an emerging area of research which aims to automate the development of ready-to-use end-to-end ML models with little to no user ML knowledge. However, existing commercially available AutoML algorithms are not well designed for minerals processing data.In this study, we develop an AutoML algorithm to develop steady-state minerals processing models suitable for mine scheduling and process optimisation. The algorithm consists of data filtering, temporal resolution alignment, feature selection, neural network architecture search, and development. The AutoML algorithm was tested on three case studies of different processes and ore types. These case studies cover the range of difficulties of possible datasets encountered in the mining and processing industry from clean simulated data to noisy data with poor correlation. The algorithm successfully developed neural network models within hours from hourly raw PI and/or daily assay data with no human intervention. These models derived from process data have minimal errors as low as < 3 % for major valuables like Ni and Cu, 6–7 % for by-products like Au, 8–10 % for deleterious minerals like MgO, and 5–8 % for gangue.The algorithm was also designed so that expert minerals processing knowledge can influence the pipeline to improve the quality of models. As a result, the AutoML algorithm becomes a powerful tool for mining and mineral processing experts to apply their domain knowledge of the process to develop models of equipment or processing circuits.

Analysis of the Influence Mechanism of Small Fault Activation under the Influence of Mining at Deep Coal Faces.

The influence mechanism of different geological parameters on the activation of small faults under mining in the deep coal face was investigated by numerical simulation. The study shows that (1) the degree of influence of six parameters including the fault lateral pressure coefficient λ, elastic modulus E, Poisson’s ratio μ, cohesion c, internal friction angle φ, and fault inclination angle θ on the activation of small faults was ranked in order as θ > λ > φ > μ > E > c. (2) The lateral pressure coefficient λ of the fault zone rock mass, the internal friction angle φ, and the fault inclination angle θ had a great influence on the fault activation. The larger the λ of the fault zone rock mass was, the smaller the φ was, the larger the subsidence amount drop of the rock layers on both sides of the fault was, and the greater the severity of the fault activation was. The fault activation degree decreased first, then increased, and then decreased with the increase of θ. When θ was 15 and 60°, there was relatively severe fault activation. When θ was 75°, the fault was not easy to activate. (3) Poisson’s ratio μ, elastic-modulus E, and cohesion c of the fault zone rock mass had little effect on the fault activation. The larger the E of the fault zone rock mass was, the larger the μ was, the smaller the c was, and the possibility of fault activation increased slightly. The research results can provide important theoretical support for the theoretical research on fault activation, on-site roof management, and mining schedule arrangement in the mining process of deep coal face.

Cemented paste backfill compressive strength enhancement via systematic water chemistry optimisation

Systematic study of the evolution of the uniaxial compressive strength (UCS) of cemented paste backfill (CPB) is presented using various mine site hypersaline water and synthetic brines. 5 and 9 wt% binder CPB were hydrated up to 56 days. CPB prepared with 1/3 hypersaline water reached the benchmark of 1 MPa at 14 and 7 days respectively for 5 and 9 wt% binder, while 2/3 and 85 % hypersaline water CPB showed improvement compared with both tap and hypersaline water. Synthetic brines replicating mine site water composition were prepared and showed similar enhanced mechanical performance (<7 days of hydration to reach benchmark). SEM-EDX probe of micro-morphology showed that cationic (Na+ and Mg2+) and anionic (Cl- and SO42-) concentrations at optimal performance produced distinct calcium silicate hydrate and ettringite formations, which combine to facilitate observed mechanical performance enhancement. Findings demonstrate systematic mixing water utilisation for CPB preparation can contribute to improved underground mine scheduling.

Optimization Production Scheduling of Underground Backfilling Mining Based on NSGA-II

Optimization Production Scheduling of Underground Backfilling Mining Based on NSGA-II

Open Pit Optimization Using the Floating Cone Method: A New Algorithm

Three-dimensional block models are the most frequently used tool for estimating mineral resources and reserves within a mineral deposit. In open pit mining, the basis of mine design and the long term mining schedule is calculation of the ultimate pit limit. The ultimate pit limit is the pit with the highest profit value. Over the years, different algorithms have been developed that enable us to calculate the final pit: floating or mobile cone, floating cone II and its corrected forms, floating cone III, the Korobov algorithm and its corrected form, the Lerchs–Grossmann 2D algorithm (dynamic programming), and the Lerchs–Grossmann 3D algorithm (graph theory). All these algorithms have advantages and disadvantages. The floating cone method stands out for its simplicity, speed, and easy implementation, even for calculating a pit with a variable slope angle. The main drawback of this method is that it is unable to examine all possible combinations. For this reason, the algorithm does not consistently give optimal results, which is why it has required improvements over time. However, the improved methods still have some problems. To overcome these problems, a new algorithm called the floating cone IV method will be demonstrated in this paper.

Short-term mine scheduling targeting stationary grades

In short-term mine planning, mining scheduling is generally defined by designing dig-lines, allocated on benches. The mined ore will be sent to stockpiles, homogenization piles, or a concentration plant. The process to design dig-lines is usually done manually, whereby multiple simultaneous mining fronts are time-consuming and labour-intensive. The manual design of dig-lines tends to produce high variability of the grades throughout certain periods. Due to the limited time to manually multiple test dig-line design alternatives in short term planning, it is impossible to ensure production under stationary mean grades and variance. This article proposes an alternative to design short-term dig-lines, through an optimization process that joins and sequences the blocks in the block model over weeks or months, ensuring low variability of grades among periods. The methodology proposed generates multiple random paths starting at seed-points representing the locations and numbers of shovels previously selected by the mine planner. It tests multiple polygons representing a set of first dig-lines, comparing them with others, and keeping the dig-lines of low variability closer to a specific ore grade probability distribution, discarding the rest of the iterations. The process is repeated for the next dig-line. The block grades' probability distribution of all iterations is compared to a reference-grade histogram, and the iterations with the grade histogram more adherent are selected. Union-find and genetic algorithms were used to optimize the dig-lines aiming at the possible stationary grade distribution. The mean and variance of the reference model are 2.13% and 0.64%2, respectively. The mean for the automated draw dig-lines is closer to these values than the ones manually drawn. The method ensures more constant quality and quantity of ore production along a period planned, matching a target grade probability distribution. The methodology is illustrated using SiO2 values at a major iron ore mine.

Integrated optimisation of short- and medium-term planning in underground mines

ABSTRACT This article describes a new model aiming at optimising short- and medium-term underground mine scheduling. The problem complexity and the frequency at which planners must revise these schedules are among the primary motivations for developing such a model. To address this problem, a Mixed Integer Programming model is proposed with a flexible time discretisation to accurately represent both shortand medium-term operational constraints in a single model. The results of a preliminary mathematical formulation are presented with in-depth analyses. Further, an improved formulation is presented with associated results and benefits. Extensive experiments with scenarios similar to long-term planning show promising results for the improved formulation. Among other things, these results show that the modified formulation makes it possible to solve for scenarios that include a large number of activities and locations while generating more applicable solutions.

Improved mine waste dump planning through integration of geochemical and mineralogical data and mixed integer programming: Reducing acid rock generation from mine waste

Although the environmental significance of acid rock drainage (ARD) generated from mining wastes is well known, selecting the appropriate ARD management strategy can prove a complicated task. Chemical methods are favored for initial mine waste characterization but using these exclusively can overlook key factors, e.g., mineralogy, which controls the formation and elution of ARD. This paper first presents an ARD waste rock classification developed on Triple Characterization Criteria (TCC) which considers three input parameters: neutralizing potential ratio (NPR), net acid generation (NAG pH), and modal mineralogy weathering index (MMWI) values. Second, a new mixed-integer programming (MIP) model to guide waste dump construction with the dual aim of preventing ARD across the life-of-mine (LOM) and reducing waste rock re-handling, is introduced. Last, the spatial distribution of TCC in a planned waste dump is simulated via geo-statistical techniques to evaluate the MIP model. The proposed waste rock classification and dump planning model has been tested at an iron mine. The results of the MIP modeling and simulation of TCC showed the successful prevention of ARD by achieving large values of TCC (NPR ≥2, NAG pH ≥ 4.5, and MMWI ≥4.7) for dump cells, with the planned mine production maintained. The integrated TCC approach introduced in this study is intended to enable mine operators, at the start of the LOM, to effectively forecast ARD from future waste rock. Further, the MIP model will facilitate development of a mine schedule that optimizes the use of the waste materials based on TCC values. If used correctly, the TCC and MIP model have the potential to enable mine operators to reduce their environmental footprint across the entire LOM.

Integrated Parametric Graph Closure and Branch-and-Cut Algorithm for Open Pit Mine Scheduling under Uncertainty

Open pit mine production scheduling is a computationally expensive large-scale mixed-integer linear programming problem. This research develops a computationally efficient algorithm to solve open pit production scheduling problems under uncertain geological parameters. The proposed solution approach for production scheduling is a two-stage process. The stochastic production scheduling problem is iteratively solved in the first stage after relaxing resource constraints using a parametric graph closure algorithm. Finally, the branch-and-cut algorithm is applied to respect the resource constraints, which might be violated during the first stage of the algorithm. Six small-scale production scheduling problems from iron and copper mines were used to validate the proposed stochastic production scheduling model. The results demonstrated that the proposed method could significantly improve the computational time with a reasonable optimality gap (the maximum gap is 4%). In addition, the proposed stochastic method is tested using industrial-scale copper data and compared with its deterministic model. The results show that the net present value for the stochastic model improved by 6% compared to the deterministic model.