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Presented by:Abhishek Barla(08MI1028) Bineshwar Bhagat(06MI1003) Saksham S Patel(08MI3021)

Study of metallic ore deposits involves the use of structural geology, geochemistry, the study of metamorphism and its processes, as well as understanding metasomatism and other processes related to ore genesis. Ore deposits are delineated by mineral exploration, which uses geochemical prospecting, drilling and resource estimation via geostatistics to quantify economic ore bodies. The ultimate aim of this process is mining.

Geometallurgy combines geological and metallurgical information to create spatially- based predictive model for mineral processing plants. A holistic particle-based approach is proposed as geometallurgy is applied in mining industry shows that the linkage of geological information and metallurgical response relies on small number of samples tested in laboratory. The approach consists of three quantitative models: 1) geological model 2) particle breakage model and 3) unit process models. The geological model describes quantitatively and spatially modal composition and texture of the ore.

The particle breakage model that describes quantitatively what kind of particles will be produced as the rocks given by the geological model are broken. The unit process models quantify how particles behave in different unit operations. Demands for more effective utilisation of orebodies and proper risk management in mining industry have emerged a new branch called geometallurgy. Geometallurgy combines geological and metallurgical information to create spatially-based predictive model for mineral processing plants. It is not really a discipline itself because it is related to certain ore deposit and certain processing flow sheet and therefore it can be rather regarded as a practical amalgamation of ore geology and minerals processing.

Geometallurgical program is an organized attempt to create reliable, practical and useful model of an ore deposit and mineral processing plant that is used to exploit the resource. Geometallurgical program goes through following steps:1) Collection of geological data through drilling, drill core logging, measurements, chemical analyses, and other analyses. 2) An ore sampling program for metallurgical testing where geological data is used in the identification of preferred locations for the samples. 3) Laboratory testing of these samples in order to extract process model parameters (sometimes called ore variability testing).

4)Checking the metallurgical validity of the geological ore-type definitions and, where necessary, developing new ore-type definitions called geometallurgical domains. 5) Developing mathematical relationships for the estimation of important metallurgical parameters across the geological database. 6) Developing a metallurgical model of the process. 7) Plant simulation using the metallurgical process model and the distributed metallurgical parameters as the data set. 8)Calibration of the models via benchmarking for existing operations.

Geometallurgy significantly reduces the impact of spatial uncertainty in mine planning because it documents the variability in a deposit. This lowers project risk by enabling: Rigorous documentation of geological and mineralogical impact on metallurgical performance and grindability Plant design that recognizes the inherent variability of the deposit. Forecasting of production parameters such as plant throughput, grade, recovery and concentrate grade on a quarterly or yearly basis, with a statistical confidence interval. Optimization of plant performance with respect to ore variability. Effective mining of the ore over the entire mine life. Optimized mine resource and plant performance. Maximize the Net Present Value whilst minimizing risk

Better utilisation of the ore resources because ore boundaries are defined also on the basis (forecasted) metallurgical performance. Better metallurgical performance because it is possible to tune the process according to the information of the plant feed beforehand. Better controlled mining due to more comprehensive knowledge of the ore body. Better changes in plant optimisation because the variation in the plant feed is low or, at least, is better controlled. Better changes for new technological solutions because ore-derived problems are identified well ahead and research programs can focus on solving these.

Lowering risks in the operation though better knowledge of the ore body and the process and through more controlled process chain. Better possibilities for economical optimising of the full operation considering metal prices, alternative products and costs of commodities.

Geostatistics deals with grade estimation. Earlier prospecting done by excavation. Then the modern drilling technology came. Earlier methods were very crude. Then geostatistics was introduced to make much more reliable prediction.

Collection of data. Semi-variogram modeling. Curve fitting. Determination of anisotropy. Kriging. Estimation of grade. Determining accuracy of prediction Making a 3D model of the ore body.

Most important use of 3D modeling is the ability to plan the mine. Now we know the best method of mineral extraction, be it underground or opencast. The grade of each face can be predicted. Through blending we can achieve the desired grade. Location of crushers can be determined. Thus the overall quantity and time duration of production will be known. Useful information for mineral processing.

Each particle in the model output has following properties: size, mineral composition by weight, mineral composition by volume, mineral composition by surface area, flowrate or mass proportion of all the particles (t/h), and (texture as a particle map, potentially).

The unit operations models used in minerals processing can be divided in three types: Comminution models where particle size distribution changes. Separation models where particles are distributed between two or more output streams based on their physical properties. Leaching and precipitation models where liquid phase is an active component and minerals dissolve and new phases are formed through chemical reactions.

1) Adam Robert Lucas (2005), "Industrial Milling in the Ancient and Medieval Worlds: A Survey of the Evidence for an Industrial Revolution in Medieval Europe", Technology and Culture 46 (1): 1-30 [10-1 & 27] 2) Run-of-mine: The raw mined material as it is delivered prior to treatment of any sort. "Dictionary of Mining, Mineral, and Related Terms". Hacettepe University Department of Mining Engineering. Retrieved 2010-08-07. 3) Applied mineral inventory. H.J. synclair.