Underground mine simulator at discrete events to support decision making.

O objetivo deste trabalho foi demonstrar a utilidade da simula??o a eventos discretos, aplicada ?s opera??es de mina subterr?nea, como ferramenta de apoio ? tomada de decis?es. Para isso, foi desenvolvido um modelo de simula??o de uma mina subterr?nea na cidade de Jacobina, utilizando o software Arena. O trabalho foi desenvolvido em 03 fases, constru??o do modelo, valida??o do modelo e estudos de mudan?as estrat?gicas sobre o sistema analisado. Ap?s a conclus?o, comparando os resultados obtidos aos resultados do sistema real, o modelo foi validado. Com o objetivo de demonstrar que a simula??o ? ferramenta eficaz para o aux?lio ? tomada de decis?o, simulou-se dois novos cen?rios, onde foram consideradas hip?teses de mudan?as estrat?gicas no sistema. A partir da an?lise dos novos cen?rios, foi poss?vel comprovar que a ferramenta simula??o a eventos discretos ? muito eficaz para suporte ? tomada de decis?o em mina subterr?nea.The approach of this study was to prove the usefulness of discrete event simulation applied to Underground Mine Operations as a support tool for decision making. The Underground Mine in Jacobina City was simulated in Arena Software. The worked was development in 03 phases: simulation model building, simulation model validation and Studies of strategic changes on the analyzed system. Comparing the results of simulation model to the real system results, the simulation model was validated. With objective to demonstrated that the discrete event simulation is the effecttive tool to support of decision making, two news scenarios were simulated, which were consided hypotheses of strategic changes in the system. From the analyses of news scenarios, it was possible to prove that the simulation tool is very effective to support the decision making analyses about Underground Mine

Incorporation of geological uncertainty in pit optimization with geostatistics simulation.

Risk mapping processes in mine planning and ore recovery are constantly used in the mining industry to increase decision making certainty based on the available information. However, it is not possible to predict the risk behavior in all of the project's boundary conditions and small variations in some of these conditions can cause a great impact on its financial return. Among the countless uncertainties existing in a mining project (operational, costs, market change), many authors define the geological uncertainty as the most critical one, capable of influencing the success of the project. Measurement and evaluation of the geological uncertainty of a mine planning project is crucial because the calculated risk can be translated into a financial risk of the project. This article presents a possible way to consider the geological uncertainty in the pit optimization step by using sequential Gaussian simulation. The results obtained from the case study on a copper deposit results in a simple procedure with significant increase in reliability for the project

Evaluation of rock slope stability conditions through discriminant analysis.

A methodology to predict the stability status of mine rock slopes is proposed. Two techniques of multivariate statistics are used: principal component analysis and discriminant analysis. Firstly, principal component analysis was applied in order to change the original qualitative variables into quantitative ones, as well as to reduce data dimensionality. Then, a boosting procedure was used to optimize the resulting function by the application of discriminant analysis in the principal components. In this research two analyses were performed. In the first analysis two conditions of slope stability were considered: stable and unstable. In the second analysis three conditions of slope stability were considered: stable, overall failure and failure in set of benches. A comprehensive geotechnical database consisting of 18 variables measured in 84 pit-walls all over the world was used to validate the methodology. The discriminant function was validated by two different procedures, internal and external validations. Internal validation presented an overall probability of success of 94.73% in the first analysis and 68.42% in the second analysis. In the second analysis the main source of errors was due to failure in set of benches. In external validation, the discriminant function was able to classify all slopes correctly, in analysis with two conditions of slope stability. In the external validation in the analysis with three conditions of slope stability, the discriminant function was able to classify six slopes correctly of a total of nine slopes. The proposed methodology provides a powerful tool for rock slope hazard assessment in open-pit mines

Analysis of sensitivity of the pit economic optimization.

Uma das vari?veis que torna um projeto de minera??o mais vulner?vel ? instabilidade econ?mica ? a varia??o dos pre?os de venda dos min?rios. Nesse trabalho, procedeu-se a uma an?lise de sensibilidade de cavas ?timas em rela??o ? varia??o do pre?o de venda de um bem mineral. Foi gerada uma s?rie de contornos de cavas ?timas com diferente valor presente l?quido (VPL) e diferentes quantidades de min?rio e est?ril, a partir da aplica??o de fatores de desconto sobre um pre?o de venda de min?rio tomado como base. An?lises desse tipo s?o ?teis para o estabelecimento do planejamento de produ??o de uma mina. Para esse estudo, foram utilizados dados da mina Morro da Mina da Rio Doce Mangan?s S.A. e, ara a gera??o das cavas otimizadas, utilizou-se o m?dulo Pit Optimiser do Gemcom Surpac 6.0.One of the variables that makes the economic stability of a mining project more vulnerable is the variation of ore sale prices. In this work, a sensitivity analysis of optimum pits in relation to the variation of the sale price of ore was performed. It generated a set of optimal pits shells with different net present values (NPV) and different amounts of ore and waste, and the application of discount factors of the ore?s sale price was taken as the base. Analyses of this type are useful for the establishment of production planning of a mine. For this study, data obtained from the manganese mine of Morro da Mina of Rio Doce Mangan?s S.A. was used, and the modulate Pit Optimiser of the Gemcom Surpac 6.0 was applied to generate the optimum pits

Regression model utilization to estimate the mass recovery of a phosphate mine in Brazil.

The construction of block models with an estimation of grades in situ is a common practice throughout resource evaluation. However, this information is not enough to understand the behavior of the ore in the beneficiation process. Geometallurgy proposes the addition of the ore?s metallurgical behavior in the block model, making it more dependable and adhering when it comes to production capacity, which generates financial earnings and brings risks down. Mass recovery is an important metallurgical variable for economic and mine planning. This is often underused, due to the lack of data, making it hard to use in the planning process. In order to achieve better use of the data available, the multiple regression analysis technique was used so as to develop a statistic model that would relate the mass recovery with the in situ grades, allowing that deposit regions with no available metallurgical information have an estimation of this variable?s values

Incorporation of geological uncertainty in pit optimization with geostatistics simulation

Abstract Risk mapping processes in mine planning and ore recovery are constantly used in the mining industry to increase decision making certainty based on the available information. However, it is not possible to predict the risk behavior in all of the project's boundary conditions and small variations in some of these conditions can cause a great impact on its financial return. Among the countless uncertainties existing in a mining project (operational, costs, market change), many authors define the geological uncertainty as the most critical one, capable of influencing the success of the project. Measurement and evaluation of the geological uncertainty of a mine planning project is crucial because the calculated risk can be translated into a financial risk of the project. This article presents a possible way to consider the geological uncertainty in the pit optimization step by using sequential Gaussian simulation. The results obtained from the case study on a copper deposit results in a simple procedure with significant increase in reliability for the project

Estimativa de produtividade horária aplicada à elaboração e execução de planos de lavra.

Para elaboração de planos de lavra, é necessário adotar parâmetros de produtividade horária dos equipamentos disponíveis para sua execução. Esses equipamentos devem estar em sincronia com a capacidade produtiva das operações unitárias de lavra mineira: perfuração de rocha, desmonte, carregamento e transporte. Esse estudo trata da estimativa de índices de produtividade horária das operações de carregamento e transporte com a utilização de regressões múltiplas. Através da utilização da base de dados do sistema de gerenciamento operacional de uma mina, foram geradas equações que explicam o ciclo produtivo de cada um dos equipamentos de carga e das frotatransporte. Conhecendo-se o comportamento das principais variáveis que exercem influência na produtividade horária, foi possível estimar esse índice e garantir alta exequibilidade para o plano de lavra, pois os índices obtidos foram muito próximos dos reais. A formulação do modelo foi realizada de forma simples, utilizando-se de conceitos estatísticos e de ferramentas computacionais, sendo o modelo sustentado por uma boa correlação entre as variáveis. Esse fator foi decisivo para que esse estudo obtivesse bons resultados na execução dos planos de lavra estudados.In order to elaborate a mine plan, it is necessary to adopt hourly production parameters for the equipment available for its execution. This equipment should be in synchrony with the productive capacity of the operational units in the mine for: rock drilling, blasting, loading and transporting ore. This study deals with the estimate of hourly production indexes for loading and transport obtained by the use of multiple regressions. Using a management system database from an operating mine, equations were generated to explain the productive cycle of each of the loading machines and dump trucks. Knowing the behavior of the principle variables that exert influence on the production schedule, it was possible to estimate the index and to guarantee high feasibility for the mine plan, considering the indexes obtained were very close to those executed. The model design was created in a simple manner, using statistical concepts and computational tools, being supported by good correlation between the variables. This factor was decisive in obtaining good results in the execution of the studied mining plan