T. J. Howard

Maintaining product grade from diverse mine sites at BHP billiton iron ore Newman joint venture

Abstract At BHP Billiton Iron Ore Newman Joint Venture (NJV) ore is railed, from four separate mine operations in the Newman area to Port Hedland, where the ore is screened, crushed and blended to produce lump and fines NJV products. Each mines grade varies separately, so it is important that a centralised system is in place to control the overall blend and ensure that the products meet customer expectations. It is important that grade related decisions are built into the normal production process and have minimal impact on production rates. A continuous stockpile management system (CSMS) covering the whole NJV production process is used to generate the daily crushing schedule taking into account all currently available ore sources. Innovative additions to the CSMS provide more accurate information to support decision making by the mine schedulers; for example, blast hole estimates are continually adjusted for bias compared to crusher and port rake grades; Lump–fines grade split and beneficiation plant upgrade algorithms are updated on a continuous basis, and the dilution effect which impacts on reported port rake grades is removed.

Simulation modelling of grade variability for iron ore mining, crushing, stockpiling and ship loading operations

Abstract 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 Excels user friendly interfaces. Presentation of the results is enhanced by Excels 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.

Predicting finished product properties in mining industry from pre-extraction data

Abstract 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.

Predicting lump and fines finished product grades and lump percentage from head grade

Abstract Cliffs Natural Resources Pty Ltd operates iron ore mines in the Koolyanobbing region of Western Australia. Ore is mined from three locations, separated by many kilometres. The ore is stockpiled at these locations, according to an in-house ore classification system (based on grade and source), and then trucked to the crushing and screening plant at Koolyanobbing. Lump and fines products are railed to Esperance for ship loading and export to Asian customers. Cliffs Natural Resources Pty Ltd prides itself on the relatively low intershipment grade variability of the products. The Koolyanobbing crusher is fed using a daily blend plan, generated to maintain lump and fines product grades within acceptable tolerance ranges around targets. Achieving low variability requires predicting lump and fines grades as accurately as practical from the estimated head grades of the Run of Mine (ROM) ores potentially going into the blend. The grade prediction model may be either a direct prediction of crushed lump and fines grades and lump percentage, or be split into two stages: the bias between blast hole estimated head grade and crusher head grade, and then the lump–fines algorithm for splitting the head grade between lump and fines products. The lump–fines algorithm comprises the percentage of lump, and the difference between the lump and fines grades. The authors describe a weighted least squares regression model for predicting crusher grades and lump proportion from the estimated head grade for Fe, P, SiO2, Al2O3, Mn and S. The method is applied to Cliffs Natural Resources Pty Ltd production data, where the regression model explains ∼60% of the variance in the crushed ore grade, for both lump and fines. A further small but significant improvement in prediction can be achieved by including the ore classification data in the model. The regression errors exhibit strong positive serial correlation, indicating trends in grade error across multiple blend records. To compensate for the longer term error, an exponentially smoothed model was developed and applied to the daily grade blend errors. This gave an increase in the longer term variance explained and therefore an improvement in grade prediction.

The Development of an MIS for Iron Ore Mining Operations

We describe the development and implementation of a management informing system (MIS) to aid the mining, transporting, stockpiling and shipping of iron ore. Ore is railed from mines to a port, where it is blended on stockpiles and recovered to ships. The project commenced as a Decision Support System to aid quality control of ore grade. It evolved to become an MIS aiding operators, decision makers and managers at multiple stages and levels of the production process. The project’s history provides valuable lessons for the development of an MIS. The project was designed and implemented with clients rather than for clients, ownership by the users was emphasized, and domain knowledge was sought and incorporated at every stage. Contrary to textbook prescription, these considerations suggest that, rather than basing a project upon some initial grand plan, an incremental evolution is preferable, with developers and users cooperatively exploring possibilities as they unfold.

Precision analysis of iron ore sampling, preparation and measurement overcoming deficiencies in current standard ISO 3085

Abstract At Cliffs Natural Resources Ltd (CNR) crushing and shipping sample stations, precisions are estimated for the sampling, preparation and measurement stages of iron ore sampling in accordance with . In this standard, gross duplicate samples are prepared at each of the three stages, yielding eight measurement assays. Appropriate calculations of the differences between assay pairs are then made to estimate precision of each stage of sampling. The international standard for checking precision in iron ore sampling, , prescribes a method for identifying outliers that is ambiguous and possibly inappropriate. The procedure also makes an unnecessary implicit assumption, that the differences between assay pairs are normally distributed. The Anderson–Darling statistic is used to demonstrate that in some cases the assay pair differences have distributions significantly different from normal. When applied at CNR as specified, the standard leads to precision estimates well below the expected values, thus overestimating the sampling method’s capability and limiting the opportunity for real process improvement. This paper suggests an improved method of estimating sampling, preparation and measurement precision. A bootstrap procedure is used to estimate confidence limits for the calculated precision estimates. The proposed method would be suitable for testing blast hole precision. The discussion is supported by extensive simulation modelling of realistic data.