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. 4 1. DRA Mineral Projects 2. Green Team International 3. Deloitte Consulting 4. Assmang Pty. Ltd.
Khumani Iron Ore Mine, South Africa Operating since 2008
You can take all the measurements you need but if you don t have easy and timeous access it does not mean much
Basic Flow Diagram
Data Accessibility Issues Slow access to production data Integrating data from multiple sources different formats, different levels of granularity Important data emailed 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
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
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
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
Example of Dashboards Created Lumpy Jigs Feed and Yields Jig Plant Performance Shift Dashboard 2014_11_17 Day Shift Lumpy Jigs Cyclone Pressure 1 400 1 200 1 000 800 600 400 200 0 1 400 1200 1216 1138 1172 1186 1072 1100 999 999 920 726 06:00 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 300 250 200 150 100 Target Stream 1 Stream 2 Stream 3 Stream 4 Total Plant Feed Product and Yield 50 Feed Target % Var Product % Yield Target % Var 13 128 18 000 27% 8 369 64% 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 500 450 400 350 300 250 200 150 100 50 0 3.8 3.8 3.0 3.0 3.0 3.0 3.0 3.0 3.0 4.0 3.9 3.9 299 286 333 307 333 242 400 467 367 304 300 301 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 4.0 3.0 2.0 1.0 0.0 100 90 80 70 60 50 40 30 20 10 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
Creating a Dashboard Footer
Creating a Dashboard
OLAP Cube
Graphically Representation of the OLAP cube When Where What - hour, shift, day, month, year - Map ID - tons, LIMS, etc.
Where - Map ID s Example of Flow Diagram with Map IDs
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
Architecture
How is the cube used practically
Typical Use Excel sheets automatically populated Daily laboratory report
Data Granularity Laboratory or the analyser calibrating team to check the results from 2 sources Enable timeous intervention
Metallurgical Uses Short term planning cycle Automatically updating short term planning tools (forecasting)
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
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
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
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
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
Khumani Iron Ore Mine, South Africa
IT solution to empower non-it users