The Use of Common Business Intelligence and Analytics Tools in the Operation and Optimisation of Iron Ore Process Plants.

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1 The Use of Common Business Intelligence and Analytics Tools in the Operation and Optimisation of Iron Ore Process Plants. Fry, M.R. 1, Nassis, T. 2, Louw, P. 3 and du Toit, T DRA Mineral Projects 2. Green Team International 3. Deloitte Consulting 4. Assmang Pty. Ltd.

2 Khumani Iron Ore Mine, South Africa Operating since 2008

3 You can take all the measurements you need but if you don t have easy and timeous access it does not mean much

4 Basic Flow Diagram

5 Data Accessibility Issues Slow access to production data Integrating data from multiple sources different formats, different levels of granularity Important data ed in spreadsheets e.g laboratory data Poor access to long term historical production data long lead time when requesting data from a vendor External consultants/contractors drain resources with data requests

6 Data Visualisation Issues (e.g. reports, dashboards) Constant report changes as new managers have different preferences Long lead time when requesting report changes from your software vendor Software vendor package limitations

7 Objectives Priority No. 1 Create a system where non-it personnel can query data and create their own dashboards or reports. 2. Establish a stable platform of data. 3. Create a unified interface for access to all production data. 4. Create Plug and Play capabilities for new vendor systems to add/remove their data. 5. Create long term continuous data storage and access. 6. For IT dept. - Reduce the burden that reporting services create on source databases

8 Solution The Use of Common Business Intelligence and Analytics Tools in the Operation and Optimisation of Iron Ore Process Plants. Microsoft Excel Everyone knows how to use it OLAP cube (On-line Analytical Processing) Technology for processing and then presenting multidimensional data for analysis Common business tool that is part of MicroSoft SQL server

9 Example of Dashboards Created Lumpy Jigs Feed and Yields Jig Plant Performance Shift Dashboard 2014_11_17 Day Shift Lumpy Jigs Cyclone Pressure :00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Feed Rate Target Target Stream 1 Stream 2 Stream 3 Stream 4 Total Plant Feed Product and Yield 50 Feed Target % Var Product % Yield Target % Var % % 81% 21% 0 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Lumpy Jigs Feed Rate and Number of Modules Running Lumpy Jigs Sump Level :00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17: Target Stream 1 Stream 2 Stream 3 Stream 4 Avg. Feed Rate per Module per Operating Hour Feed Rate Tgt No. of streams running 0 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00

10 Creating a Dashboard Footer

11 Creating a Dashboard

12 OLAP Cube

13 Graphically Representation of the OLAP cube When Where What - hour, shift, day, month, year - Map ID - tons, LIMS, etc.

14 Where - Map ID s Example of Flow Diagram with Map IDs

15 What - Measurements Measurement Device Weightometer Online Chemical analysis Laboratory Analysis of samples Online Particle Size Analyser Flowmeter Densitometer Pressure Indicator Level indicator Measurement (s) Mass flow on conveyor belt Real time chemical analysis: %Fe, etc. Delayed Chemical analysis: %Fe, etc. Delayed Particle size distribution of the sample Particle size distribution of material lying on the conveyor belt Volumetric flow Slurry density Line pressure Tank Level

16 Architecture

17 How is the cube used practically

18 Typical Use Excel sheets automatically populated Daily laboratory report

19 Data Granularity Laboratory or the analyser calibrating team to check the results from 2 sources Enable timeous intervention

20 Metallurgical Uses Short term planning cycle Automatically updating short term planning tools (forecasting)

21 Metallurgical Uses Met Accounting tools have been created using the OLAP cube in order to: monitor the reliability of the weightometer readings, check the mass balance across the plant and its sub-sections, checking the reliability of the online sizing and chemical analysers, perform investigations as required

22 Typical Investigation (e.g. Have the solids to the tailings dam increased?) Before Diagrams need to be scrutinised for the correct instrument tags Hours are spent gathering and organising all the data 1000 s of rows of data Level of granularity has to be decided up front and then can t be changed Using the OLAP cube New calculation is made in the cube Data can be viewed for any period Level of granularity can be changed as required No data required, can go straight to chart

23 Finding Required Data Data analysts with little knowledge of the plant query data and create reports with Excel Example of Flow Diagram with Map IDs

24 Consultants on site Consultants on site can be shown the cube to access the data themselves No training required No reliance on mine resources Create their own dashboards as required

25 IT Advantages Permanent storage of data separate from the production systems (e.g. production capturing, mine truck and dispatch systems, laboratory information management systems, etc.). Relieves the production systems from reporting workload. The OLAP cube technology offers faster access to data for BI systems NO licensing fee to pay for 3 rd party vendors

26 Khumani Iron Ore Mine, South Africa

27 IT solution to empower non-it users

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