Fernando Gambin

Estratégia de controle de qualidade de minérios na lavra utilizando simulação geoestatística

The new generation of coal fired power plants in Brazil are expected to use ROM coal as fuel. Thus, variations of coal quality tend to be transferred from mine to the final user. Contracts frequently establish max-min limits for geological and technological parameters. Lots of ore with quality beyond the agreed limits can be rejected or penalized. The high costs of these penalties require quality control of the final product (ROM). The utilization of geostatistical methods aims for quality and variability characterization of ore in the deposit. The geostatistical method uses a block model with grades assigned to each block normally by ordinary kriging. This block model ignores or is inappropriate for accessing the uncertainty associated with the geological model. Consequently, this procedure fails in predicting grade fluctuations correctly. Contrary to kriging, simulation methods aim at reproducing in situ grade variability and spatial continuity. Once a block model has each grade uncertainty evaluated, quality fluctuation can be predicted for a given mining route and size of lot delivered to the customer. Different mining routes and sizes of ore lots are tested until a desirable level of grade oscillation is achieved. Results from a case study at a Brazilian coal mine proved the adequacy and functionality of the method. Simulation geostatistical makes possible predictions of quality fluctuations at certain volumes of ore in the deposit.

Forecasting fluctuations in coal quality delivered to a power plant via stochastic simulation

Abstract The new generation of coal-fired power plants in Brazil are expected to use run-of-mine (ROM) coal as fuel; however, variations in the quality of the coal are likely to be transferred from the mine to the final user. Contracts frequently establish maximum/minimum limits for geological and technological parameters, allowing for rejection or imposition of penalties when the quality of the lots is outside the agreed limits. The high costs associated with such penalties necessitate quality control of the final product (ROM). Geostatistical methods can be used to predict in situ ore quality and variability. Geostatistical methods employ a block model with grades assigned to each block. The block model obtained through the use of ordinary kriging is inappropriate in accessing the uncertainty associated with the geological or technological parameters being modelled. Consequently, this procedure fails to predict grade fluctuations correctly. Contrary to kriging, simulation methods aim at reproducing in situ grade variability and spatial continuity. Once one produces a block model, with each grade uncertainty evaluated, quality fluctuation can be predicted for any given mining route. Also the fluctuations associated with the size of a lot to be delivered to the customer can be predicted. Different mining routes and sizes of ore lots are tested until the desired level of grade oscillation is achieved. Results from a case study at a Brazilian coal mine prove the adequacy and functionality of the method and that geostatistical simulation adequately predicts quality fluctuations.

Estimativa de incerteza na classificação de recursos minerais por simulação geoestatística

O sucesso de um novo empreendimento de mineracao depende muito da recuperacao de tonelagens e teores estimados, usando informacoes obtidas durante campanhas de exploracao mineral. Essas estimativas deveriam ser capazes de alertar para possiveis altos riscos na classificacao de recursos, possivelmente construidas usando intervalos de confianca associados com cada estimativa. As categorias de recursos minerais sao definidas de acordo com o espacamento entre amostras e com o grau de confiabilidade em cada classe de recurso, a ser medida, indicada e inferida. Varios sistemas de classificacao estao disponiveis, mas, para esse estudo, o sistema JORC (Joint Organisation Reserves Committee) foi utilizado. Esse artigo propoe uma metodologia para verificar a incerteza associada com volumes e tonelagens relacionados no inventario de um deposito mineral. Para ilustrar a metodologia, um deposito de carvao no sul do Brasil foi utilizado. Nesse deposito, estavam disponiveis 340 furos de sondagem com dados de espessura e 236 com informacoes sobre densidade. Os resultados forneceram ferramentas para medida da incerteza baseadas em um procedimento com embasamento teorico.