Mine Scheduling Research Articles

РОЗРОБКА ТА РЕАЛІЗАЦІЯ МОДЕЛІ ДЛЯ ОБҐРУНТУВАННЯ ОПТИМАЛЬНИХ ТЕХНОЛОГІЧНИХ СХЕМ ВІДПРАЦЮВАННЯ ВИКИДОНЕБЕЗПЕЧНИХ ВУГІЛЬНИХ ПЛАСТІВ

Purpose. The purpose of the work is to develop a new approach to substantiate the optimal technological schemes for the mining of outburst-prone coal seams, which will ensure the minimum likelihood of sudden outbursts of coal and gas in the breakage face. Methodology. The integrated approach has been applied, involving the use of graphs, network models, optimization algorithms to find optimal technological solutions; application of energy-force models to assess the likelihood of sudden outbursts of coal and gas; application of dynamic programming to substantiate technological parameters (seam thickness, cutter-loader web width, length of a breakage face, etc.) for mining of outburst-prone coal seams. Results. In the course of the work, a new mathematical model has been obtained and implemented, which allows assessing the risks of a sudden outburst of coal and gas during the mining of coal seams. Based on the application of the model, an algorithm for substantiating technical and technological solutions has been developed. Technical solutions provide for the choice of means of mechanization of a breakage face, a mining schedule out a breakage face, measures for degassing a coal seam, while technological solutions provide for the justification of the length of a breakage face, the cutter-loader web width, and the speed of movement of the breakage face line. Scientific novelty. The originality lies in the creation of the mathematical model that allows assessing the likelihood of a sudden outburst of coal or gas, which made it possible to create a new approach based on the use of graphs and network models, as well as to determine the optimal technological solutions for mining coal seams. Practical significance. The practical value of the work lies in the creation of an application package for the selection of purification facility in accordance with certain technological solutions. The general algorithm for justifying technological schemes for mining outburst-prone coal seams involves the following sequence of actions: setting the initial parameters of a rock mass and breaking them into discrete values; for possible routes of the graph of technological solutions, the mathematical expectation and dispersion are determined; the probability of sudden outbursts of coal and gas for the studied parameters is determined; on the basis of the calculated probability, dependences of the occurrence of a coal or gas outburst are obtained in accordance with the values of the parameters (face length, the speed of movement of the breakage face line, etc.); on the basis of the data obtained, the optimal technological schemes for mining outburst-prone coal seams are substantiated; based on the application of a package of applied programs, mechanization means are selected. The scope of the above approach is not limited to the existing means of mechanization, mining and geological conditions of coal seams, existing technological schemes, and others.

Mixed Integer Linear Programming Model for Open Pit Mine Scheduling

An open-pit mines consist of the accumulation of ore layers with different chemical characteristics and aims to extract ores from the surface. Based on a geological discretization of the deposit, the ore body model is represented as a three-dimensional array of blocks divided into two subsets; ore blocks, which can be extracted and processed profitably, and waste blocks, which include all the remaining blocks. The extraction process results from the iteration of a sequence of elementary operations performed on blocks by specialized or multipurpose machines. Short-term planning of the ore extraction process in open-pit mines is a complex undertaking that necessitates the identification of the best spatio-temporal machinery affectation. All of this is constrained by several factors: operations sequencing, machines compatibility, machines availability, machines motions, and priorities of layers (urgency of obtaining certain qualities). In this study, a Mixed Integer Linear Programming model (MILP) is used to generate the short-term extraction schedule. The MILP is based on the well-known flexible job-shop theoretical problem with generic minimal time-lags between operations. A simple example is used to clearly explain the model and the solution obtained.

Application of numerical modelling method for determination of underground mining’s impact on constructions in G9 coal storage at Mong Duong coal mine by using program UDEC2D

The coal extraction in longwall creates voids that destroy the natural stress balance in underground. This causes deformation and failure of rock and consequently damage of surface construction and environment. In Vietnam, surface construction damage caused by underground mining is quite common such as fan station at level +142 m Mao Khe coal mine, water burst at roadway level -60 m Thong Nhat coal mine, road crack near Yen Tu pagoda, or surface subsidence at Khe Cham coal preparator. Due to the displacement magnitude and nature of damaged constructions such as private house, industrial building or railway, a coal mine may need to stop production and suffers great economic loss. The East wing of L7 coal seam is medium in thickness and gentle-inclined in dip angle. It completely underlays the G9 storage at Mong Duong coal mine. According to the mining schedule, this seam is planned to be extracted by drilling-and-blasting method and be supported by self-moving hydraulic frame GK. This poses a safety risk to equipment operating in the storage. It is extremely necessary to find a reasonable mining solution to ensure the safety of G9 coal storage constructions and minimize the loss of resources for this L7 coal seam. In this paper, the author uses numerical modelling method to determine the impact of underground mining on surface constructions in G9 coal storage at Mong Duong coal mine.

Application of risk assessment to improve sustainability in bauxite mining

Uncertainty and risk management are essential for decision-making in the sustainable extraction of mineral resources. Uncertainty in mine planning typically starts with the geostatistical simulation of the variables that influence the decision process, which produces a series of equally probable models. Then a transfer function is applied to each model. The result is a distribution of the response function, which calculates the probability (or risk) that a given area achieves the product quality standards. High-risk operations often fail to achieve the ore quality standards and decrease the mineral resources value. In this context, failure in managing risk may collapse mining operations, leading to unrepairable economic, environmental, and social damages. However, risk assessment is a challenge in the case of multivariate deposits such as bauxite deposits. In these deposits, the response function depends on multiple variables simultaneously. The variables that characterize bauxite deposits also exhibit complex relationships and sum and fraction constraints. The geostatistical simulations should reproduce these peculiarities. This work proposes a workflow to measure risk and uncertainty in complex multivariate deposits aiming at sustainable gains for the mining operations. The workflow starts with a multivariate geostatistical simulation using Projection Pursuit Multivariate Transform. Then these simulated models are combined with the initially planned dig lines and mining scheduling. The results informed the dig lines and periods with the highest risks and uncertainty for all variables, allowing the decision team to take measures focusing on sustainable goals. The methodology is illustrated in a case study of a significant bauxite deposit located in northern Brazil.

Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations

Comminution circuits can be modelled using phenomenological models of individual unit operations to represent the operation performance. Process optimisation and block model variability analysis require millions of simulations to fully explore process efficiency through mine scheduling which is costly and time consuming. Surrogate modelling is a technique used in process engineering approach of approximating the behaviour of the underlying model by using a model which is computationally more feasible. Neural networks are a form of machine learning which can approximate complicated function mappings of inputs to outputs and are computationally parallelisable.In this study, a neural network is used as a surrogate model to approximate a copper porphyry mine comminution circuit for faster simulations. The neural network was trained to predict throughput using data generated from the phenomenological models of the comminution circuit. The optimal neural network hyperparameters were determined using an evolutionary algorithm to minimise overfitting. The neural network predicted simulation results 3363 times quicker than phenomenological models with errors of 0.37%, 0.55% and 0.45% across three different test sets. The neural network only required a stratified training sample of 1 in 1000 data to interpolate the rest of the data.

INVESTIGASI DAN REKAYASA TAPAK REKLAMASI: STATE OF ART SUATU SOLUSI UNTUK MENUNJANG PASCATAMBANG TIMAH ALLUVIAL YANG BERKELANJUTAN

Reklamasi pada area bekas tambang merupakan salah satu tantangan untuk merepresentasikan wujud nyata manajemen berkelanjutan pada sektor pertambangan Indonesia. Kerangka kerja peraturan reklamasi dan pasca tambang di Indonesia menetapkan berbagai persyaratan untuk melaksanakan kegiatan reklamasi dan pasca tambang. Sebagai langkah awal, para pemangku kepentingan diwajibkan untuk berkonsultasi secara detail tentang peraturan-peraturan yang berlaku. Reklamasi timah alluvial (dan kemungkinan endapan logam alluvial lainnya) merupakan masalah yang pelik dalam mewujudkan program reklamasi pasca tambang yang berkelanjutan. Sering kali program reklamasi gagal karena adanya proses penambangan kembali di lahan yang sudah direklamasi. Masalah utama dalam proses reklamasi timah alluvial adalah bahaya geologi, hidrologi, hidrogeologi dan penambangan kembali. Dengan adanya data-data yang baik maka akan dapat dilakukan rekayasa teknik yang baik untuk mewujudkan reklamasi yang berkelanjutan. Penerapan teknologi tepat guna untuk mengantisipasi bahaya geologi, hidrogeologi, hidrologi serta monitoringnya sehingga aspek yang akan mengurangi keberhasilan program reklamasi sudah dapat diantisipasi sejak awal sehingga program pencegahannya dapat dilakukan sejak dini. Proses sterilisasi yang baik dilakukan sejak awal proses penambangan ataupun dilakukan sebelum proses penutupan tambang agar proses reklamasi dapat berjalan secara berkelanjutan.

Development of Muruntau–Myutenbai joint open pit mine

Muruntau open pit mine has been in operation since 1967 and is one of the largest open pit mines in the world. As per the project of mining phase V effective since 2020, it is among the world’s three largest open pit mines. The distribution of ore reserves per horizons is analyzed with regard to nature-and-process zones delineated in Muruntau–Myutenbai joint open pit mine. The project parameters of the pit and the scope of mining operations were determined from the geological and mathematical modeling including the detailed survey data and borehole assay results. The open pit mining scenarios within the project limits of mining phase V up to 2030 are considered. The open pit mining scheduling is performed with regard to operational specifics of the deposit, number of nature-and-process zones and duration of the scheduling period. It is concluded that maintenance of the required gold content of ore to be processed needs stimulation of the annual mining output up to 50–60 Mm3 in the easterly direction of the pit at the preserved production output in all nature-and-process zones. To this effect, it is required to formulate a sustainable action plan, with the assigned performance indicators, with regard to the Extra Activity Progr am to Improve Production of Precious Metals at Navoi Mining and Metallurgical Combinat over the period to 2026 and further on to 2040. The authors appreciate participation of Head of a Department at VNIPIpromtekhnologii, Candidate of Engineering Sciences A. V. Seleznev in this study.

Optimal substitution rate analysis of submarine natural gas hydrates considering geological and hydrological conditions

In order to improve the ability of data mining and information scheduling for the best replacement rate of seabed natural gas hydrate under geological and hydrological conditions, an optimization algorithm for estimating the best replacement rate of seabed natural gas hydrate is proposed. By using piecewise linear estimation method, the characteristic data of submarine gas hydrate database are grouped and reconstructed, and the optimal replacement rate of submarine gas hydrate in the database is fused with related information. According to the spatial distribution of geological and hydrological conditions, the optimal replacement rate of submarine gas hydrate is adaptively dispatched. The optimal replacement rate of submarine gas hydrate is extracted by frequent item mining method, and the extracted optimal replacement rate of submarine gas hydrate is processed in turn. Finally, the unbiased estimation of the optimal replacement rate of submarine gas hydrate is realized by using regularized power spectrum estimation method. The experimental results show that the design method has small deviation and high accuracy in estimating the best substitution rate of submarine natural gas hydrate.

Underground mine scheduling under uncertainty

Underground mine schedules seek to determine start dates for activities related to the extraction of ore, often with an objective of maximizing net present value; constraints enforce geotechnical precedence between activities, and restrict resource consumption on a per-time-period basis, e.g., development footage and extracted tons. Strategic schedules address these start dates at a coarse level, whereas tactical schedules must account for the day-to-day variability of underground mine operations, such as unanticipated equipment breakdowns and ground conditions, both of which might slow production. At the time of this writing, the underground mine scheduling literature is dominated by a deterministic treatment of the problem, usually modeled as a Resource Constrained Project Scheduling Problem (RCPSP), which precludes mine operators from reacting to unforeseen circumstances. Therefore, we propose a stochastic integer programming framework that: (i) characterizes uncertainty in duration and economic value for each underground mining activity; (ii) formulates a new stochastic variant of the RCPSP; (iii) suggests an optimization-based heuristic; and, (iv) produces implementable, tactical schedules in a practical amount of time and provides corresponding managerial insights.

A mathematical model for the scheduling and definition of mining cuts in short-term mine planning

Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives.

Automatic closed-loop scheduling in underground mining using discrete event simulation

SYNOPSIS Today's mining operations require fast reporting and rapid rescheduling based on updated information. An automatic mine scheduling system could not only quickly reschedule but also propose alternative solutions. To avoid the financial and physical risks associated with testing such a system directly in operation, it could be first evaluated via discrete event simulation models. This would offer a safe environment to evaluate different operating rules and algorithms. In this study, this is achieved by integrating automatic scheduling software with a discrete event simulation model. Keywords: discrete event simulation; production planning; scheduling system; underground mining.

Developing a mathematical model to optimize long - term quarrying planing for limestone quarries producing cement in Vietnam

Mathematical model researches in optimizing long - term quarrying planning for limestone quarries supplying to the cememt factories have not been popular in Vietnam. The paper shows a mathematical model based on Mixed Integer Linear Programming as well as effective method to cope with long - term quarrying planning for the quarries. Techniques grouping block cells into a mining room and decision variable elimination techniques was employed to reduce the size of the problem. This enable to form an optimal method with proper time. The model and the algorithm are an effective tool to conduct a long - term planning for the quarries, enabling to supply the quatity and quality enough of raw materials to the factory. The model was demonstrated and practically evaluated when being applied and compared with one of the popularly commercial sofwares in mine schedule, GEOVIA Mines.

Value chain excellence – managing variability to stabilise and exploit the mine value chain

ABSTRACT Mining occurs in highly variable, spatially dynamic environments. Existing methods of planning and operations management are insufficient to adequately account for variability. Managing variability within the value chain aims to maximise business value. This paper presents a methodology for managing variability. This includes: assessing the impact of variability on a deterministic mine schedule; applying guiding principles based on Theory of Constraints to stabilise and exploit the value chain; developing a risk approach to minimise costs of plan delivery; and establishing tools for critical path planning, reconciling performance against plan and establishing corrective actions to deliver plan outcomes. A case study is presented from the Olympic Dam mine. Conclusions include: operations contain significant variability that if not managed will fail to deliver the mine plan; the proposed methodology can be applied to reduce variability and robustly deliver the best-value mine plan; and this methodology has broad application across the mining industry.

Underground mine scheduling of mobile machines using Constraint Programming and Large Neighborhood Search

Manual short-term scheduling in underground mines is a time-consuming and error-prone activity. In this work, we present a Constraint Programming approach capable of automating the short-term scheduling process in a cut-and-fill mine. The approach extends previous work by accounting for fleet travel times, and thus captures an important aspect of the real-world machine scheduling problem. We introduce two models: one that directly solves the original interruptible scheduling problem, and one that is based on solving a related uninterruptible scheduling problem and transforming its solution back to the original domain. Large Neighborhood Search is also employed with a domain-specific neighborhood definition that helps to find high-quality schedules faster. Problem instances derived from an operational mine are used to demonstrate the efficacy of our approach.

A simulation–optimization framework for short-term underground mine production scheduling

Mine operations are supported by a short-term production schedule, which defines where and when mining activities are performed. However, deviations can be observed in this short-term production schedule because of several sources of uncertainty and their inherent complexity. Therefore, schedules that are more likely to be reproduced in reality should be generated so that they will have a high adherence when executed. Unfortunately, prior estimation of the schedule adherence is difficult. To overcome this problem, we propose a generic simulation–optimization framework to generate short-term production schedules for improving the schedule adherence using an iterative approach. In each iteration of this framework, a short-term schedule is generated using a mixed-integer linear programming model that is simulated later using a discrete-event simulation model. As a case study, we apply this approach to a real Bench and Fill mine, wherein we measure the discrepancies among the level of movement of material with respect to the schedule obtained from the optimization model and the average of the simulated schedule using the mine schedule material’s adherence index. The values of this index decreased with the iterations, from 13.1% in the first iteration to 4.8% in the last iteration. This improvement is explained because the effects of the operational uncertainty within the optimization model can be considered by integrating the simulation. As a conclusion, the proposed framework increases the adherence of the short-term schedules generated over iterations. Moreover, these increases in the adherence of schedules are not obtained at the expense of the Net Present Value.

Evolutionary approach for large-Scale mine scheduling

Finding the optimum solution for open-pit mine scheduling problems (OPMSPs) is a well-known challenging problem in the mining industry. The existing methodologies for solving large-scale OPMSPs are mostly limited to conventional optimization approaches and heuristics. However, due to the problem’s complexity, represented by high-dimensionality, and hard and soft constraints, the current approaches face challenges in finding good quality solutions with a reasonable computational cost. As an alternative approach, in this paper, we propose an evolutionary approach, based on differential evolution, with three important features: to deal with high-dimensionality, based on the extraction status of each block, we reduce the number of decision variables (blocks) over the planning horizon; to deal with the complex constrained landscape, we develop a repair mechanism that guarantees feasibility and to enhance the algorithm’s performance, we incorporate a local search technique. The experimental results on well-known mine deposits, with up to 112, 687 blocks show that the algorithm has the edge over existing methods to obtain better solutions.

Design and Implementation of Geographic Information System Based on Environmental Dynamics in Mine Scheduling

With the development of computer technology, it is possible to visualize the demand of mine safety and production process. At the same time, the demand of mine resources for social construction and the people’s lives has been showing a rapid upward trend, which invisibly brings opportunities and challenges to the development of mine undertakings. Under the dynamic environment, mine production, transportation, ventilation, gas, power supply and industrial television operation are monitored at any time, and enterprise panoramic information is displayed from multiple angles, so as to realize timely shunt processing and tracking of dispatching information. User staff can obtain real-time information such as location, thus ensuring the safety of mining personnel and property, improving work efficiency and enterprise benefits, and further promoting the design key components of the environmental dynamic geographic information system in mine scheduling. With environmental dynamics as the regulating variable, geographic information system is an information system that collects, stores, manages, describes and analyzes data related to the earth’s surface, space and geographic distribution.

Direct sequencing of blocks in stochastic models with multi-mines and multi-destinations

Abstract Mining Scheduling is the one that maximizes profit from mining over time. By means of computational methods, the deposit is discretized in blocks and algorithms are used to consummate this objective. The methods that are widely known nowadays for mining scheduling optimization of a discrete block model were based on graph theory, and among those most used by the mining industry is a solution found by Lerchs and Grossmann (1965) which, together with other methods, was consolidated as the process of traditional mining planning. For both supply and multi-destination models, there is a limitation of the current methodology, since it consists of the optimization of each mine separately which may not be a global optimization solution. This article proposes an optimization of the benefit in a stochastic model through the DBS (Direct Block Scheduling) for a copper mining complex with two mines, a pre-existing copper stockpile and two treatment streams, comparing several scenarios and analyzing the best alternative for the proposed problem.

Optimization of a SAG Mill Energy System: Integrating Rock Hardness, Solar Irradiation, Climate Change, and Demand-Side Management

Integration of renewable energy into mining and processing operations is becoming necessary as part of a strategy towards sustainability in the minerals industry. A solar photovoltaic plant along with a battery energy storage system (PV-BESS) can provide a long-term solution to cope with increasing energy costs, thus reducing the tension with other societal competing needs for a key resource such as clean energy. However, sizing these systems is challenging, in face of uncertain ore grindability and solar power availability. In this paper, we present an application of an integrated model to size the PV-BESS, where the variability of ore grindability is modeled using geostatistical tools, solar irradiance variability is captured using a Markov chain simulation model, and the entire system is optimized through linear stochastic optimization, considering a fixed mine schedule for the feed of a semiautogenous grinding (SAG) mill. The main goals are to minimize the costs associated with operating the SAG mill in the presence of a PV-BESS system, understand how the sizing and costs change under the influence of stochastic drivers, and reveal the potential for demand-side management in mines. The size and costs of the necessary infrastructure are determined for a model of estimated grindability and an ensemble of 50 simulated models of grindability, and compared against the sizing and cost of the ground truth. The effect of climate change on solar energy availability is accounted for by forecasting the ratio of excellent, good, and moderate days over bad days in terms of irradiance out to the year 2030. Finally, the effects of stockpiles and feed control according to the processing plant needs, namely a demand-side management approach, are evaluated to reveal the impact on the energy requirements and the sizing of the photovoltaic and storage system. The model is optimized considering a yearly cost function, with hourly resolution for the solar irradiance and hardness models. The results show that integrating solar power into the operation of a SAG mill has potential to reduce the total energy cost by 27%. Robustness against climate change can be achievedwith an increase in total cost of 1%. Finally, use of stockpiles to manage the ore supply to the mill and minimize the energy cost to process it results in a cost reduction of around 2%, which should offset the rehandling cost of managing the stockpiles.

GIS information feature estimation algorithm based on big data

In order to improve the data mining and information scheduling capabilities of geo-information system (GIS), it is necessary to optimise GIS information feature estimation and perform GIS information feature extraction, so a GIS information feature estimation algorithm based on big data analysis is proposed. In this algorithm, the piecewise linear estimation method is adopted to reconstruct feature data in the GIS information database in group, and associated information fusion is performed to the GIS data in the database, and adaptive scheduling is performed to the GIS information feature database through the cascaded distributed scheduling method; according to the spatial distribution of geographic information, vector adjustment is performed to the cluster centre, and the frequent item mining method is adopted to extract features of GIS information, and then sequential processing is adopted to the extracted feature quantity of GIS information; the regularised power density spectrum estimation method is adopted to perform unbiased estimation to GIS information feature data. Simulation results show that in GIS information feature estimation, the proposed method can provide estimation with low bias and high accuracy, so it has good GIS information scheduling capability and precision.