685 research outputs found

    Określanie Optymalnej Wartości Odcięcia Zawartości Procentowej Pierwiastka Użytecznego W Złożach Siarczku Miedzi

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    Optimum cut-off grades determination in mining life affects production planning and ultimate pit limit and it is also important from social, economical and environmental aspects. Calculation of optimum cut-off grades has been less considered for mines containing various mineral processing methods. In this paper, an optimization technique is applied to obtain optimum cut-off grades for both concentration and heap leaching processes. In this technique, production costs and different recoveries of heap leaching method directed into modeling different annual cash flows in copper mines. Considering the governing constraints, the Lagrange multiplier method is practiced to optimize the cut-off grades in which the objective function is supposed to maximize Net Present Value. The results indicate the effect of heap leaching process on the optimum cut-off grades of primary and secondary sulfide deposits

    Mineral Asset Valuation Under Economic Uncertainty: A Complex System for Operational Flexibility

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    The global mineral industry faces constant challenges that are incited and intensified by market uncertainty. Demand constrictions, resource scarcity, and market volatility all generate market risk that is compounded by the high capital and long payback periods inherent to mining projects. Quantitative risk assessments provide a methodology to leverage uncertain economic scenarios and accurately assess profitability; however, current mine valuation techniques and engineering economic approaches tend to scrutinize the uncertainty of technical factors, such as ore grade and metallurgical recovery, to a much greater degree than market factors, like price-demand restrictions. Nevertheless, the optimal operating conditions for mining, mineral processing and refining must reflect the true dynamics of uncertain commodity prices, and typical operational responses, such as modifications to mine production and material stockpiling.;This thesis presents a new mineral asset valuation methodology based on economic uncertainty in the commodity market and operational flexibility for mining operations. This novel valuation approach resulted in the generation of a complex system that consists of three primary components. First, a price forecasting component was used to generate future commodity price scenarios with two different stochastic differential equation models (Geometric Brownian Motion and Mean-Reverting-drift). Second, a dynamic methodology of discounted cash flow (DCF) was developed, allowing operational flexibility for mining, processing, stockpiling, and selling material. Third, two distinct optimization techniques (Interior-point method and genetic algorithms) were applied for identification of optimal operating parameters in a mining operation, with a particular focus on using buffer stockpiles to ameliorate the impacts of volatile price fluctuations. The dynamic model was applied in a case study assessing the valuation of a greenfield Ni-Co-Sc mine project. The hypothetical deposit was subjected to different levels of commodity price trends, price volatility, discount rates and maximum stockpiling capacity. Overall, the dynamic valuation model obtained NPV results ranging from 2% to 11% higher than standard static DCF techniques. Operational flexibility and ore inventory management proved to be crucial for profit increase on the project

    Study on batching and picking optimization of marine outfitting pallets

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    Outfitting pallet picking involves the retrieval of items from their storage sites in shipbuilding enterprises. A major issue in manual pallet picking operations is the transformation of outfitting pallets into picking batches (pallet batching). Considering the influence to the subsequent distribution and production processes, a mathematical model for the batching and picking problem of outfitting pallets is formulated with the objective of minimizing the total tardiness of all pallets. According to the characteristics of outfitting pallet picking operations, an Improved Genetic Algorithm (IGA) is proposed. A reversal operator is specially introduced to increase the local search ability of the standard genetic algorithm and speed up the evolution. Benchmarked against the solutions produced by the Earliest Due Date (EDD) rule, the performance of IGA is studied under different picking operations with different workloads and tightness of due dates. A series of numerical experiments are carried out to verify the researches. The results clearly show that IGA is competitive since it improves the solutions by 68.5%, on average, relative to the EDD

    Advances in Computational Intelligence Applications in the Mining Industry

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    This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

    Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy

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    Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy held in Šibenik, Croatia, June 21-26, 2020. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

    Modelo de optimización estocástica de la ley de corte para depósitos polimetálicos

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    La incertidumbre en los proyectos de inversión en minería asociada con la volatilidad de precios y la variabilidad en los costos han llevado a que este tipo de inversiones estén involucradas en condiciones de riesgo hacia futuro. Por esta razón y con el fin de maximizar el retorno en las inversiones, la ley de corte crítica se ha convertido en el parámetro de decisión que determina la viabilidad económica de cada proyecto. Sin embargo, la metodología para la selección de la ley de corte optima sobre la cual se han estado desarrollando todos los modelos de optimización de ley de corte que se han trabajado a lo largo de las últimas ha sido basada en modelos determinísticos que no tienen en cuenta el riesgo y la incertidumbre inmersos en la actividad minera. Con lo anterior entonces se hace necesario desarrollar un enfoque estocástico con el que se abarquen escenarios de variabilidad que se ajusten a la realidad que se tiene en este tipo de negocios y sobre los cuales se logren analizar los diferentes componentes que influyen en las tres restricciones planteadas por Lane en el cálculo de la ley de corte: mina, planta y mercado. Por tal razón la idea principal del trabajo es el planteamiento de un modelo optimización estocástica del algoritmo de Lane desde el punto de vista del mercado aplicado a un proyecto de Cu y Mo y sobre el cual sea posible analizar los diferentes escenarios de incertidumbre asociados a la variabilidad de los precios de estos metales.Abstract: Uncertainty in mining investment projects associated with price volatility and variability in costs has led to risks regarding future conditions. For this reason and in order to maximize return on the investment, the cut off grade has become the decision parameter that determines the economic viability of each project. However, the methodology that has been used for the selection of the optimal cut-off grade and has been worked over the last decades has been based on deterministic models that do not take into account the risk and uncertainty involved in mining. Based on that, it has become necessary to develop a stochastic approach covering variability scenarios that are more related with the reality of this business, analyzing the different components that influence the three constraints posed by Lane calculation of the cut-off grade: mine, plant and market. For this reason the main idea of this work is the approach a stochastic optimization algorithm of Lane model from the point of view of the market, applied to a project of copper, molybdenum and gold (Cu y Mo) and about how it is possible to analyze the different scenarios of uncertainty associated with the variability of prices of these metals.Maestrí

    Determination of an Ultimate Pit Limit Utilising Fractal Modelling to Optimise NPV

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    The speed and complexity of globalisation and reduction of natural resources on the one hand, and interests of large multinational corporations on the other, necessitates proper management of mineral resources and consumption. The need for scientific research and application of new methodologies and approaches to maximise Net Present Value (NPV) within mining operations is essential. In some cases, drill core logging in the field may result in an inadequate level of information and subsequent poor diagnosis of geological phenomenon which may undermine the delineation or separation of mineralised zones. This is because the interpretation of individual loggers is subjective. However, modelling based on logging data is absolutely essential to determine the architecture of an orebody including ore distribution and geomechanical features. For instance, ore grades, density and RQD values are not included in conventional geological models whilst variations in a mineral deposit are an obvious and salient feature. Given the problems mentioned above, a series of new mathematical methods have been developed, based on fractal modelling, which provide a more objective approach. These have been established and tested in a case study of the Kahang Cu-Mo porphyry deposit, central Iran. Recognition of different types of mineralised zone in an ore deposit is important for mine planning. As a result, it is felt that the most important outcome of this thesis is the development of an innovative approach to the delineation of major mineralised (supergene and hypogene) zones from ‘barren’ host rock. This is based on subsurface data and the utilisation of the Concentration-Volume (C-V) fractal model, proposed by Afzal et al. (2011), to optimise a Cu-Mo block model for better determination of an ultimate pit limit. Drawing on this, new approaches, referred to Density–Volume (D–V) and RQD-Volume (RQD-V) fractal modelling, have been developed and used to delineate rock characteristics in terms of density and RQD within the Kahang deposit (Yasrebi et al., 2013b; Yasrebi et al., 2014). From the results of this modelling, the density and RQD populations of rock types from the studied deposit showed a relationship between density and rock quality based on RQD values, which can be used to predict final pit slope. Finally, the study introduces a Present Value-Volume (PV-V) fractal model in order to identify an accurate excavation orientation with respect to economic principals and ore grades of all determined voxels within the obtained ultimate pit limit in order to achieve an earlier pay-back period.Institute of Materials, Minerals and Mining, the global network IOM3Cornish Institute of EngineersWhittle Consulting (Business Optimisation for the Mining Industry

    Ecodesign of large-scale photovoltaic (PV) systems with multi-objective optimization and Life-Cycle Assessment (LCA)

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    Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environmental assessment technique was used based on Life Cycle Assessment (LCA). The environmental model was successfully coupled with the design stage model of a PV grid-connected system (PVGCS). The PVGCS design model was then developed involving the estimation of solar radiation received in a specific geographic location, the calculation of the annual energy generated from the solar radiation received, the characteristics of the different components and the evaluation of the techno-economic criteria through Energy PayBack Time (EPBT) and PayBack Time (PBT). The performance model was then embedded in an outer multi-objective genetic algorithm optimization loop based on a variant of NSGA-II. A set of Pareto solutions was generated representing the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., Principal Componet Analysis, PCA) was then applied to detect and omit redundant objectives that could be left out of the analysis without disturbing the main features of the solution space. Finally, a decision-making tool based on M-TOPSIS was used to select the alternative that provided a better compromise among all the objective functions that have been investigated. The results showed that while the PV modules based on c-Si have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. TF PV modules present the best trade-off in all scenarios under consideration. A special attention was paid to recycling process of PV module even if there is not yet enough information currently available for all the technologies evaluated. The main cause of this lack of information is the lifetime of PV modules. The data relative to the recycling processes for m-Si and CdTe PV technologies were introduced in the optimization procedure for ecodesign. By considering energy production and EPBT as optimization criteria into a bi-objective optimization cases, the importance of the benefits of PV modules end-of-life management was confirmed. An economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making

    Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

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    This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data

    Stochastic-optimization of equipment productivity in multi-seam formations

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    Short and long range planning and execution for multi-seam coal formations (MSFs) are challenging with complex extraction mechanisms. Stripping equipment selection and scheduling are functions of the physical dynamics of the mine and the operational mechanisms of its components, thus its productivity is dependent on these parameters. Previous research studies did not incorporate quantitative relationships between equipment productivities and extraction dynamics in MSFs. The intrinsic variability of excavation and spoiling dynamics must also form part of existing models. This research formulates quantitative relationships of equipment productivities using Branch-and-Bound algorithms and Lagrange Parameterization approaches. The stochastic processes are resolved via Monte Carlo/Latin Hypercube simulation techniques within @RISK framework. The model was presented with a bituminous coal mining case in the Appalachian field. The simulated results showed a 3.51% improvement in mining cost and 0.19% increment in net present value. A 76.95yd³ drop in productivity per unit change in cycle time was recorded for sub-optimal equipment schedules. The geologic variability and equipment operational parameters restricted any possible change in the cost function. A 50.3% chance of the mining cost increasing above its current value was driven by the volume of material re-handled with 0.52 regression coefficient. The study advances the optimization process in mine planning and scheduling algorithms, to efficiently capture future uncertainties surrounding multivariate random functions. The main novelty includes the application of stochastic-optimization procedures to improve equipment productivity in MSFs --Abstract, page iii
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