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

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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

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Khumani Iron Ore Mine, South Africa

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You can take all the measurements you

need but if you don’t have easy and timeous

access it does not mean much

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• 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

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• Constant report changes as new managers have different

preferences

• Long lead time when requesting report changes from your software vendor

• Software vendor package limitations

Data Visualisation Issues

(e.g. reports, dashboards)

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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

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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

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Example of

Dashboards

Created

Feed Target % Var Product % Yield Target % Var

13 128 18 000 ‐27% 8 369 64% 81% ‐21% Jig Plant Performance Shift Dashboard 2014_11_17 Day Shift Total Plant Feed Product and Yield Lumpy Jigs Feed and Yields Lumpy Jigs Cyclone Pressure Lumpy Jigs Feed Rate and Number of Modules Running Lumpy Jigs Sump Level 1 138 _{1 072} 999 ₉₂₀ 999 726 1 200 1 400 1 100 1 216 1 172 1 186 0 200 400 600 800 1 000 1 200 1 400 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 0 50 100 150 200 250 300 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Target Stream 1 Stream 2 Stream 3 Stream 4 0 10 20 30 40 50 60 70 80 90 100 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Target Stream 1 Stream 2 Stream 3 Stream 4 299 286 333 307 333 242 400 467 367 304 300 301 3.8 3.8 3.0 3.0 3.0 3.0 3.0 3.0 3.0 4.0 3.9 3.9 0.0 1.0 2.0 3.0 4.0 0 50 100 150 200 250 300 350 400 450 500 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Avg. Feed Rate per Module per Operating Hour Feed Rate Tgt No. of streams running

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Creating a Dashboard

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Graphically Representation

of the OLAP cube

• When

- hour, shift, day, month, year

• Where

- Map ID

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Where

- Map ID’s

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What - Measurements

Measurement Device Measurement (s)

Weightometer Mass flow on conveyor belt

Online Chemical analysis Real time chemical analysis: %Fe, etc.

Laboratory Analysis of samples • Delayed Chemical analysis: %Fe, etc.

• Delayed Particle size distribution of the sample

Online Particle Size Analyser Particle size distribution of material lying on the

conveyor belt

Flowmeter Volumetric flow

Densitometer Slurry density

Pressure Indicator Line pressure

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• Excel sheets automatically populated

– Daily laboratory report

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Data Granularity

• Laboratory or the analyser calibrating team to check the

results from 2 sources

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Metallurgical Uses

Short term planning cycle

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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,

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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

Typical Investigation

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Finding Required Data

Data analysts with little knowledge of the plant query data and create reports with Excel

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