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Jupp K.,Mineral Resources | Howard T.J.,Ore Quality Pty Ltd | Everett J.E.,University of Western Australia
Transactions of the Institutions of Mining and Metallurgy, Section B: Applied Earth Science | Year: 2014

Pre-crusher stockpiles are designed principally as buffers to decouple the mining and processing operations. They are usually paddock dumped or dumped over a face to form fingers by dumping haul truckloads and reclaimed by front-end loader in an ad hoc manner. In addition, they are often not built and reclaimed to completion but continually added to and extracted from. Whereas this design suits the mining process and is operationally simple, there can be little confidence in the grade of the ore that is fed to the crusher. This makes short-term grade control difficult and reconciliation back to the mine face imprecise. Well-designed and operated pre-crusher stockpiles can overcome these deficiencies. The different types of grade variability in mining are reviewed. The role of pre-crusher stockpiles in reducing short-term variability is discussed and their ineffectiveness in addressing long-term variability is highlighted. Their role in removing the serial correlation of the extracted ore is explained. Pre-crusher stockpiles carry out the four, at times competing, objectives of storing, buffering, blending and grade separation. Their actual design and operation result from a compromise between these four competing roles. These roles are explained in detail along with the advantages and disadvantages of the different types of precrusher stockpiles. The recommended blended-in blended-out stockpile (BIBO) design is discussed in detail. Matched pairs of BIBO stockpiles of limited tonnage built and reclaimed to completion retain the best knowledge of grade and facilitate the reconciliation process. Simulation studies have been carried out on BIBO stockpiles to understand the blending opportunities of the different options for building and reclaiming, and to identify the methods that achieve maximum blending. It was found that laying down rows in one direction and reclaiming across these rows gives the best blending. Matching the width of the reclaim face with the daily crushing requirements is also important. The length of the rows, and building in either one or two layers has little effect on the extent of blending. Appropriate design can provide a reasonable compromise between the four objectives. © Institute of Materials, Minerals and Mining and The AusIMM 2014. Source


Everett J.E.,University of Western Australia | Howard T.J.,Ore Quality Pty Ltd
Transactions of the Institutions of Mining and Metallurgy, Section B: Applied Earth Science | Year: 2012

An essential requirement of product quality control in the mining industry is to be able to reliably predict key quality properties of finished product from the data available before the extraction of the ore. From a production viewpoint, the unit of data collection is generally the input and output data set for each shift of crusher production but could be any period where mine pre-crusher data can be reliably matched with product data. Linear regression models can be used to predict crush grades from blast grades, even where the crush material is blended from multiple sources or pits for each of which differing regression models might apply. The best model for any application will be a balance between required predictability, available data and the tolerance of the business for complex models. The regression modelling approach has several advantages over the classic method of run of mine crusher trials. The models can use any predictor variable such as grade, geotype and in situ density provided the pre-extraction data can be reliably matched with post-crusher data and is significant as a predictor. The models have been used extensively in the generation of the daily crusher plan with the aim of maintaining finished product grade. This approach has also been used associated with exploration drilling and long term planning. It is acknowledged that there are inherent problems in fitting lump and fines grade to a linear model. However, these problems are minor when such information is used for interpolation within the window spanned by the shift blend records used to produce the model. This paper discusses some of the issues limiting linear regression models in this application, and suggests methods enabling consistent models to be formulated. © 2012 The Australasian Institute of Mining and Metallurgy Published by Maney on behalf of the Institute of Materials, Minerals and Mining and The AusIMM. Source


Everett J.E.,University of Western Australia | Howard T.J.,Ore Quality Pty Ltd
GeoMet 2011 - 1st AusIMM International Geometallurgy Conference 2011 | Year: 2011

An essential requirement of product quality control in the mining industry is to be able to reliably predict key quality properties of finished product from the data available before the extraction of the ore. From a production viewpoint, the unit of data collection is generally the input and output data set for each shift of crusher production, but could be any period where mine precrusher data can be reliably matched with product data. This paper demonstrates that linear regression models can be used to predict crush grades from blast grades, even where the crush material is blended from multiple sources or pits for each of which differing regression models might apply. The advantage of using increasingly complex regressions models to match the available data to give increasingly more powerful models is examined. The best model for any application will be a balance between required predictability, available data and the business' tolerance for complex models. The regression modelling approach has several advantages over the more classic method of run-of-mine (ROM) crusher trials, which will be discussed. The models can use any predictor variable such as grade, geotype and in situ density provided the preextraction data set can be reliably matched with post-crusher data and is significant as a predictor. The models have been used extensively in the generation of the daily crusher plan with the aim of maintaining finished product grade. This approach has also been used associated with exploration drilling and long-term planning. It is acknowledged that there are inherent problems in fitting lump and fines grade to a linear model. However, these problems are minor when they are used for interpolation within the window spanned by the shift blend records used to produce the model. This paper discusses some of the issues limiting linear regression models in this application, and suggests methods enabling consistent models to be formulated. Source


Jupp K.,Mineral Resources | Howard T.J.,Ore Quality Pty Ltd | Everett J.E.,University of Western Australia
Transactions of the Institutions of Mining and Metallurgy, Section B: Applied Earth Science | Year: 2014

Simulation modelling is a practice commonly used in the mining industry to evaluate alternative process designs. Such modelling is typically undertaken as an optimisation study to increase the efficiencies, productivity and product quality of operating mines. In this situation real short-term grade variability data of extracted ore are available from production records as input data into the simulation. There is also a need for simulation modelling to be performed before mines are approved for construction to clarify the grade variability characteristics that can be expected from the operating mine and assist in the optimisation of the process design. In this situation no historical short-term grade variability data are available. To achieve meaningful and reliable results from a simulation it is necessary to have input data for the ore to be extracted from the pits, representative of the short-term grade variability that would be expected for the operating mine. This paper describes an example of a path taken to generate realistic input data. A method described as composite cut-off criterion was used to distinguish ore from waste which gave substantially greater recovered tonnage at target grade compared to the conventional quadrant cut-off grade criteria. The only data available for the project was resource model data in the form of kriged block models, which are known to underestimate true ore grade variability. To achieve realistic results from the simulation it was essential to increase this variability while maintaining the accurate average grades. Areas of certain deposits that were modelled using both kriging and conditional simulation estimation techniques were quantitatively compared to establish the comparative variance and the kriged data was modified to match the conditionally simulated variance. A realistic mining model was generated via a process of discretisation and regularisation of the resource blocks. Quantitative assessment demonstrated that this method adequately compensated for ore dilution and that adjustment for ore loss was not required due to an ore skin surrounding the edge blocks. The conditioned data were then used to generate a schedule of daily mine extraction, considering grade variability, tonnage, equipment constraints and extensive blended-in-blended-out pre-crusher stockpiles to feed into the process design simulations. For the pre-crusher stockpiles a number of alternative allocation criteria were examined for ore being extracted from the pits to identify the best method in achieving reduced variability through the daily scheduling system. The study concluded that a single analyte separation criterion produced acceptable variability with minimal complexity. The blending efficiency of manually stacked and reclaimed pre-crusher stockpiles was studied to determine a realistic blending efficiency within the pile. A recommended method of building and reclaiming was determined to give maximum blending efficiency. Finally, the data were used as input into process design simulation models; simulation from crusher feed to ship loading demonstrated that control of shipment grade variability was achievable and that the conditioning of the data delivered realistic results. © 2014 Institute of Materials, Minerals and Mining and The AusIMM. Source


Everett J.E.,University of Western Australia | Howard T.J.,Ore Quality Pty Ltd | Jupp K.,Cliffs Natural Resources
Transactions of the Institutions of Mining and Metallurgy, Section A: Mining Technology | Year: 2010

Cliffs Natural Resources Pty Ltd (CNR) operates iron ore mines in the Koolyanobbing region of Western Australia, ∼50 km north of the town of Southern Cross. Ore is trucked from three geographically isolated sources to the crusher at Koolyanobbing, where it is blended before and during crushing. Lump and fine products are produced and railed to Esperance for ship loading and export to Asian customers. The CNR is examining alternative processing paths, from mining to ship loading, with the aim of improving efficiency and reducing costs. Modifications to the system must be consistent with potential future expansions and maintain the low intershipment grade variability on which CNR prides itself and has built a strong relationship with its customers. In searching for the optimum process design, many options from mine face to ship loading must be evaluated and compared. Pilot plant studies are infeasible, while complex mineralogical interactions, competing goals and numerous possible system configurations limit the applicability of theoretical analysis. It was therefore concluded that simulation modelling would provide the confidence to take the next step into production trials. This paper describes techniques applied at CNR to simulate grade variability resulting from potential process design changes. The simulation models are easily run Excel based modules, with each module representing a different part of the process. The modules use extensive Visual Basic macros driven by Excel's user friendly interfaces. Presentation of the results is enhanced by Excel's excellent graphical capabilities. The simulation software stores and graphically presents time stamped data from a run, enabling detailed analysis of different process configurations. Final success of a simulation run is measured by intershipment variability (standard deviation and process capability) and in process ore tonnages. Meaningful results from the simulations require that the initial input data contain the same correlations present in the real production environment, between the mineral components, production linkages and across time. The data also have to allow simulation of potential changes to mining method and introduction of new pits into the blend. Mining data from the real operations under study are therefore used, with average grades and variability adjusted to match potential future development proposals. It is also necessary to filter out medium and long term variations from the production data, as this variability is best controlled through the conventional medium to long term mine planning process, not by the process design being studied. The filtering was carried out using a Fourier transform technique, which is described. For reasons of commercial confidentiality, detailed data, costs and quantitative conclusions are not reported in this paper. © 2010 Institute of Materials, Minerals and Mining and The AusIMM. Source

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