Download or read book Optimization Techniques and their Applications to Mine Systems written by Amit Kumar Gorai and published by CRC Press. This book was released on 2022-09-30 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the fundamental and theoretical concepts of optimization algorithms in a systematic manner, along with their potential applications and implementation strategies in mining engineering. It explains basics of systems engineering, linear programming, and integer linear programming, transportation and assignment algorithms, network analysis, dynamic programming, queuing theory and their applications to mine systems. Reliability analysis of mine systems, inventory management in mines, and applications of non-linear optimization in mines are discussed as well. All the optimization algorithms are explained with suitable examples and numerical problems in each of the chapters. Features include: • Integrates operations research, reliability, and novel computerized technologies in single volume, with a modern vision of continuous improvement of mining systems. • Systematically reviews optimization methods and algorithms applied to mining systems including reliability analysis. • Gives out software-based solutions such as MATLAB®, AMPL, LINDO for the optimization problems. • All discussed algorithms are supported by examples in each chapter. • Includes case studies for performance improvement of the mine systems. This book is aimed primarily at professionals, graduate students, and researchers in mining engineering.

Download or read book Optimization of Underground Mining written by Virginia Polytechnic Institute and State University. Department of Mining and Minerals Engineering and published by . This book was released on 1964* with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Applications of Simulation and Optimization Techniques in Optimizing Room and Pillar Mining Systems written by Angelina Konadu Anani and published by . This book was released on 2016 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) test the hypothesis that heuristic pre-processing can be used to increase the computational efficiency of branch and cut solutions to the BILP problem of R&P mine sequencing. A DES model of an existing R&P mine was built, that is capable of evaluating the effect of variable panel width on the unit cost and productivity of the mining system. For the system and operating conditions evaluated, the result showed that a 17-entry panel is optimal. The result also showed that, for the 17-entry panel studied, four shuttle cars per continuous miner is optimal for 80% of the defined mining segments with three shuttle cars optimal for the other 20%. The research successfully incorporated risk management into the R&P production sequencing problem, modeling the problem as BILP with block aggregation to minimize computational complexity. Three pre-processing algorithms based on generating problem-specific cutting planes were developed and used to investigate whether heuristic pre-processing can increase computational efficiency. Although, in some instances, the implemented pre-processing algorithms improved computational efficiency, the overall computational times were higher due to the high cost of generating the cutting planes"--Abstract, page iii.

Download or read book Advances in Applied Strategic Mine Planning written by Roussos Dimitrakopoulos and published by Springer. This book was released on 2018-01-17 with total page 800 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of papers on topics in the field of strategic mine planning, including orebody modeling, mine-planning optimization and the optimization of mining complexes. Elaborating on the state of the art in the field, it describes the latest technologies and related research as well as the applications of a range of related technologies in diverse industrial contexts.

Download or read book Mine Plan Optimisation written by Martin L. Smith and published by CRC Press. This book was released on 2015-12-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a range of tools for optimization in the mineral industry, including examples with mathematical programming. Topics covered include a survey of the application of optimisation technologies; a review of optimisation algorithms with math programming, math modelling and an introduction to AMPL (a widely used math modelling language), applications of linear programming to business improvement studies, surface mining applications, underground mining applications and options analysis. The math programming and AMPL is supported by the www.ampl.coma and by the author’s site, www.minesmith.com.au. This volume is intended for course use and was developed for 3rd and 4th year university education in mine planning and design or mine optimisation. Engineers, planners and policy makers in the minerals industry may find it beneficial for their professional activities as well.

Download or read book Ore Reserve Estimation and Strategic Mine Planning written by Roussos Dimitrakopoulos and published by Springer. This book was released on 2015-11-15 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: The mining business faces continual risks in producing metals and raw materials under fluctuating market demand. At the same time, the greatest uncertainty driving the risk and profitability of mining investments is the geological variability of mineral deposits. This supply uncertainty affects the prediction of economic value from the initial valuation of a mining project through mine planning, design and production scheduling. This book is the first of its kind, presenting state-of-the-art stochastic simulation and optimization techniques and step-by-step case studies. Quantification of geological uncertainty through new efficient conditional simulation techniques for large deposits, integration of uncertainty to stochastic optimization formulations for design and production scheduling and the concurrent management of risk are shown to create flexibility, options and oportunities, increase asset value, cashflows and return on investment. New approaches introduced include resource/reserve risk quantification, cost-effective drilling programs, pit design and long-term production scheduling optimization with simulated orebodies, ore reserve classification, geologic risk discounting, waste managing and demand driven scheduling, risk assessment in meeting project production schedules ahead of mining, risk based optimal stope design, options valuation when mining. Applications include commodities such as gold, copper, nickel, iron ore, coal and diamonds.

Download or read book Novel Optimization Models for Surface and Underground Mine Planning written by Yuksel Asli Sari and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Mine planning and optimization affect efficiency, profitability and productivity of operations significantly. Low commodity prices, high resource degredation maintenance costs and high fixed infrastructure costs necessitate the use of optimal decision making tools for mining companies to make profit. All mines have different characteristics and planning phases. In this research, different optimization problems that suit various mining techniques and planning stages are studied. In essential, there are two types of mining: surface mining and underground mining. Surface mining operations are generally long-term because overburden must be removed to access the profitable orebody. This requires strategic long-term planning at the feasibility stage. The first publication in the scope of this research focuses on long-term surface mine planning with environmental considerations. The provided solution optimizes the problem using mixed integer linear programming (MILP). When operation starts and bench sectors are mined on a daily basis, the need for short term planning arises. The second publication addresses the dig-limit optimization problem, which is an important part of short-term planning. With the proposed MILP optimization method, the ore-waste boundaries are delineated with the equipment size constraints. Although underground mining also starts with exploration and resource estimation/simulation stages, the problems that need to be addressed are very different from surface mining techniques and it has its own unique challenges. Special focus is given to the sublevel stoping underground mining technique. Stope optimization is a complex problem, comprised of two sub-problems: stope layout optimization and stope sequencing. MILP formulations of stope layout optimization are impractical because of the large size of the problem. In the third and fourth manuscripts, two different heuristic stope layout optimization algorithms are presented where the former uses a clustering heuristic to identify stopes with high grade concentration and the latter uses a greedy heuristic based on dynamic programming to solve the sub-problems and explore the promising stope combinations. Fifth manuscript tailors the greedy heuristic algorithm to poly-metallic mines with pillars. Both heuristic approaches are shown to be near-optimal through comparing with developed novel MILP formulations case studies in smaller problem instances. When the stope layout is finalized, the sequence can be optimized to yield the optimal project value. In the sixth and final manuscript within the scope of this research, the stope sequencing problem is formulated in MILP. To account for risk emerging from geological uncertainties, chance constrained programming is implemented. This approach maximizes the expected net present value of the operation while minimizing the deviations from the expected value due to ore grade uncertainty. It focuses the search on a unique direction based on the specified desired project risk level." --

Download or read book Technical and Economical Optimization of Surface Mining Processes written by Raheb Bagherpour and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Reliability Availability Maintainability and Safety Analysis and Optimization of Mine Power Systems written by Mokkapati Chinnarao and published by . This book was released on 1979 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Stochastic Optimization of Stope Design and Long term Underground Mining Production Scheduling for Sublevel Open Stoping Mining Operations written by Matheus Furtado E. Faria and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Underground mine planning defines the design of technically producible economic material volumes and development openings, the sequence of multiple underground activities, and the material destinations within a mineral deposit throughout the mine's life, aiming to maximize the net present value (NPV). Due to the existence of different mining methods and the inherent operational and computational complexities, this planning is commonly performed through a stepwise optimization process in which the stope layout is preliminarily designed. Subsequently, the network of developments interconnecting the production areas is conceived, defining the precedence of underground mining activities. Finally, the strategic underground mine production scheduling is optimized, the only step that accounts for the time value of money. Available optimization methods have been focused separately on each of the planning steps, which do not benefit from the synergies between the planning steps. Additionally, most of the previously mentioned methods are deterministic; that is, they neglect many sources of uncertainty, which has been extensively demonstrated to have a significant impact on the profitability and feasibility of mining operations. Therefore, this thesis proposes stochastic optimization methods for underground mines by employing a sublevel open stoping mining method and ultimately attempts to integrate the mine design and production scheduling into a single optimization framework. The first part of this thesis presents a stochastic optimization method of stope design along with the commonly used sequential underground mine planning framework. A set of geostatistical simulations is used to quantify the variability and uncertainty of grades within the mineral deposit. The proposed method aims to maximize the undiscounted profit while capitalizing on the upside potential in terms of recoverable metal of the generated stope layout. It also accounts for the development costs of potential production levels and stopes. The application of this stochastic approach at an underground gold mine achieved a 40% higher undiscounted profit and 21% recoverable metal when benchmarked against an industry-standard deterministic stope design software tool. The second part of this thesis presents an integrated stochastic optimization of stope design and long-term underground mine production scheduling by integrating time-dependent development costs and production targets. The mathematical model seeks to maximize the NPV from the scheduled stopes, as well as to minimize the shaft, drifts and crosscuts development costs and maintenance costs to keep the levels in operation while managing the risk of failing to meet yearly productions targets. Therefore, an optimal underground mine design is yielded as an output from the optimized production schedule. The method also opens new avenues to account for time-dependent sources of uncertainty that cannot be incorporated into the stope design optimization in the sequential underground mine planning framework. A case study at an underground gold mine demonstrates that the proposed method generates more selective stopes and physically different production levels, which correspond to an 11% higher NPV and a shorter life-of-mine by two years, as compared to the sequential optimization framework"--

Download or read book Reliability Availablity Maintainability and Safety Analysis and Optimization of Mine Power Systems written by Mokkapati Chinnarao and published by . This book was released on 1979 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mine to Mill Optimization of Aggregate Production written by and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Mine-to-Mill optimization is a total systems approach to the reduction of energy and cost in mining and processing. Developed at the Julius Krutschnitt Mineral Research Center in Queensland, Australia, the Mine-to-Mill approach attempts to minimize energy consumption through optimization of all steps in the size reduction process. The approach involves sampling and modeling of blasting and processing, followed by computer simulation to optimize the operation and develop alternatives. The most promising alternatives are implemented, and sampling is conducted to quantify benefits. In the current project, the primary objective was to adapt Mine-to-Mill technology to the aggregates industry. The first phase of this work was carried out at the Bealeton Quarry near Fredericksburg, Virginia. The second phase was carried out at the Pittsboro Quarry south of Chapel Hill, North Carolina. Both quarries are operated by Luck Stone Corporation of Richmond, Virginia. As a result of the work, several conclusions can be drawn from the project which should assist DOE in assessing the applicability of the Mine-to-Mill approach to the aggregates industry. 1. Implementation of MTM guidelines at Pittsboro has resulted in tangible improvements in productivity. It is clear that MTM guidelines represent an energy savings of around 5% (primary and secondary) and an overall energy savings of 1%. This 1-5% energy savings is also consistent with simulated results for Bealeton had side-by-side shots used to evaluate the technology been carried out in the same rockmass. 2. Luck Stone clearly runs their operations at a high standard. Hence the percentage improvement realized in this project may represent the lower end of what might be expected overall in the aggregates industry. 3. Variability in ore types across both Bealeton and Pittsboro suggests a 2:1 difference in hardness which contradicts the misconception that quarry rock is homogenous. Therefore, the idea of comparing side-by-side blasts is not viable and long term comparisons stand the best chance of confirming the benefits of optimized blasting. 4. There are clear limitations on how much improvement can be made in the aggregate industry due to the fixed feed size that reports to the tertiary section of a typical aggregate plant. These limitations restrict the MTM approach from exercising significant increases in blasting which would only serve to increase fines and reduce product yield. 5. The key to success at Pittsboro was the development of MTM guidelines for the modified blasting practice in consultation with the drill & blast crew. Their full buy-in was necessary to implement optimized blasting in a sustained manner. 6. The JKSimBlast and JKSimMet models have proven to be effective tools for examining blasting and processing at Bealeton and Pittsboro. These models should enable Luck Stone to transfer the MTM approach to other sites.

Download or read book Applications of Statistical and Machine Learning Models in Mining Engineering written by Siyi Li and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Statistical and machine learning models are useful tools that can be used to extract valuable information from raw data and make accurate predictions and can be applied in the optimization of mining related systems through various means. This thesis aims to further contribute to the applications of such techniques in mining engineering by providing 4 different cases where statistical and machine learning models could facilitate design and decision making. Principal component analysis (PCA) was used to reduce the dimensions of the problem and simplify the design of stockpiles in bed-blending operations, generalized linear models (GLM) were introduced to model non-linear relationships among variables in quality control and safety related problems, factor analysis methods including structural equation models (SEM) were presented to be used in conjunction with cognitive work analysis to better analyze the underlying structures or latent variables in operational health and safety in mining operations, and finally clustering, which is a family of unsupervised learning methods, was applied to a mine planning problem to integrate mining and mineral processing and maximize recovery"--

Download or read book An Integrated Optimization Tool with Applications in Mining Using a Discrete Rate Stochastic Model written by Asim Khan and published by . This book was released on 2011 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: The simulation as a stand alone optimization tool of a complex system such as a vertical integrated mining operation, significantly over simplifies the actual picture of the system processes involved resulting in an unaccountable effort and resources being spent on optimizing Non Value Added (NA) processes. This study purposed to develop a discrete stochastic simulation-optimization model to accurately capture the dynamics of the system and to provide a structured way to optimize the Value Added (VA) processes. The mine operation model to be simulated for this study is designed as a hybrid level throughput model to identify the VA processes in a mining operation. This study also allows a better understanding of the impact of variation on the likelihood of achieving any given overall result. The proposed discrete stochastic simulation- optimization model provides the ability for a process manager to gain realistic understanding of what a process can do if some factors constraining the process were to be optimized i.e. to conduct what-if analysis. Another benefit of this approached technique is to be able to estimate dependable and reasonable returns on a large optimization related expenditure. The inputs into the model are the capability of the processes which are entered using various variables depending on how much information is available; simple inputs for least amount of information to detailed inputs for well known process to combinational inputs for somewhere in between. The process bottlenecks are identified and measured using the outputs of the model which include production output, severity of constraints, capacity constraints and cumulative bottleneck plots. Once a base case has been identified and documented then the inputs can be modified to represent the business initiatives and the outputs can be compared to the base case to evaluate the true value of the initiative.

Download or read book On Globally Optimizing a Mining Complex Under Supply Uncertainty written by Luis Montiel Petro and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Mining complexes are generally comprised of multiple deposits that contain several material types and grade elements, which are transformed in available processing destinations and transported to final stocks or ports as saleable products. These components, associated with a mining complex, encompass multiple sequential activities: (i) Mining the material from one or multiple sources; (ii) blending the material including stockpiling; (iii) transforming the material in different processing destinations considering operating modes; (iv) transporting the transformed material to final stocks or ports. Since these activities are strongly interrelated, their optimization must take place simultaneously. Conventional mining optimization methods suffer from at least one of the following drawbacks when optimizing mining complexes: some decisions are assumed when they should be dynamic (operating modes, destination of mining blocks, etc.); component based objectives are imposed, which might not coincide with global objectives; many parameters are assumed to be known when they are uncertain. Past research works have demonstrated that geological uncertainty is the main cause of the inability of meeting production targets in mining projects. This thesis presents methods to optimize mining complexes that simultaneously consider different components and account for geological uncertainty. A multistage methodology that uses simulated annealing algorithm to generate risk-based production schedules in mining complexes with multiple processing destinations is presented and implemented in Escondida Norte (Chile) copper dataset. Its implementation using Escondida Norte dataset generates expected average deviations of less than 5% regarding mill and waste targets, whereas a mine production schedule generated conventionally over a single estimated model generates expected average deviations of 20 and 12% for mill and waste targets respectively. An iterative improvement algorithm that considers operating modes at different processing destinations is developed and applied to a copper complex. The objective function seeks for maximizing discounted profits along the different periods and scenarios (orebody simulations). The implementation of the method at a copper deposit allows reducing the expected average deviations from 9 to 0.2% regarding the capacity of the first process while increasing the expected NPV by 30% when compared with an initial solution generated conventionally. A method that uses simulated annealing at different decision levels (mining, processing and transportation) is described and tested in a multipit copper operation. The implementation of the method in a multipit copper operation permits the reduction of the expected average deviations from the capacities at two mills from 18-22% to 1-3% and the expected average deviation from the targets regarding two blending elements from 7-1.8% to 0.3-0.6% when compared to an initial solution generated conventionally. The expected NPV also improves by 5%. The previous method is extended to mining complexes that combine open pit and underground operations and it is tested in a gold complex in Nevada. The implementation of the method at Twin Creeks gold complex in Nevada shows improvements in meeting the metallurgical blending requirements while increasing the expected NPV by 14%. The formulations described in this thesis encompass a large number of integer variables given the discretization of the mineral deposits. To solve the problems, efficient optimization algorithms are implemented with significant improvements when compared with conventional deterministic approaches. These algorithms outperform conventional methods regarding expected NPV and meeting targets at the different components of the value chain." --

Download or read book Optimization of the Design of Open Pit Mining Systems written by Beverly Chew Duer and published by . This book was released on 1962 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book GLOBAL OPTIMIZATION OF THE OPEN PIT MINING COMPLEX WITH INTEGRATED CUT OFF GRADE OPTIMIZATION UNDER UNCERTAINTY written by and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : The mining complex refers to an integrated problem where the material is extracted from the mines; the extracted material is passed through a series of processing facilities connected with various material handling methods to generate a set of finished products, which can be sold to the customers. The optimization of the mining complex refers to the simultaneous optimization of the multiple mine production schedules, the destination of the materials, and the method of processing throughout the life of the project. The purpose of optimizing the mining complex is to deal with the effective management of resources and maximize cash flows to generate higher profits over the life of the project. The goal of this dissertation is to develop a global optimization methodology that integrates geological (supply) uncertainty and can manage the risk in the design, mining complex operations, and maximize the cash flows. In this study, a new production schedule approach is presented that integrates geological uncertainty and generate the extraction sequence for the mining complex problem. The extraction sequence is developed to maximize the net present value and provide a consistent quantity of the material to different destinations. To optimize the quantity of materials sent to different destinations, the destination policies are defined based on the cut-off grade optimization and block economic values. This allows to form the destination policies for the mined material into various processing streams and maximize the value of the operation. The production schedule and the destination policies are optimized within a unified solution approach for the mining complex problem. The work presented advances the field through the development of the new model that uses the combination of maximum flow, genetic algorithm, and Lane's method for the global optimization of the mining complex. The method simultaneously optimizes the production schedule and the cut-off grade while considering uncertainty. The performance advantages and limitations are analyzed and tested on real-world examples. The results show that the models reduce the production risks and increase the net present value of the mining operation.