CWIN17 Toulouse / Industrial big data and mes, the winning combination to improve industrial performance n.praizelin-n.monnet-v2

Industrial Big Data and MES,
the winning combination to
improve industrial performance
Nicolas Monnet, Rio Tinto
Nathalie Praizelin, Capgemini
Toulouse, 28th September
#CWIN17

September 28th 2017 │ Toulouse
Copyright © 2017 Capgemini and Sogeti. All rights reserved. 2
#CWIN17
Rio Tinto, a global mining & metals company
North
America
Africa
Europe
South
America
Australasia
Asia
We operate in 35 countries
and employ 50,000 people.
4 product groups:
• Iron Ore
• Copper & Diamonds
• Energy & Minerals
• Aluminium

#CWIN17
The Rio Tinto Aluminium group
Bauxite Alumina Aluminium
• Our benchmark smelting technology: AP Technology™
• Aluminium Pechiney is the French subsidiary of Rio Tinto Aluminium (RTA).
• The R&D teams of AP are the core of the RTA Technology and the facilities are located
close to Grenoble (French Alps).
4 mines (35 Mt/year) 10 smelters (2.5 Mt/year)6 refineries (9 Mt/year)

#CWIN17
Electrolysis process
2 Al2 O3 + 3 C 4 Al + 3 CO2
Alumina: ~1 900 kg
Carbon: ~ 400 kg
Aluminium: ~1 000 kg
CO2: ~ 1 300 kg

#CWIN17
Typical aluminium smelter
Electrolysis
400 pots in line
400 000 Amps
400 000 tons aluminium/year
Casthouse
Freeze liquid aluminium in various
products (ingots, slabs, billets, rod, etc.)
Carbon
Produce the carbon blocks (anodes)
for the electrolysis pots
Electrical
Sub-station

#CWIN17
Our industry 4.0 vision for the Aluminium plants
Data Analytics tools
Process Optimization, Predictive
Maintenance, Predictive Quality
Generate
alert Operation Center
New sensors & massive data
acquisition
Process supervision
Supply chain optimization
Develop
tools
Excellence Center
Advanced problem solving
Data Analytics tools development
Standardized
IT & Automation
(high business content)
Process Control
Manufacturing Execution System
Advanced Operator
Mobility & Wearable intelligence
3D vision & Augmented reality
 Connect shop floor to centers
UAV & UGV
AGV & Automated Cranes

#CWIN17
Illustration with industrial Big Data
DATA LAB
Key equipment
Main motors
Electrolysis, Carbon, Casthouse
Value added products
Anode quality
Process Optimization Predictive QualityPredictive Maintenance

#CWIN17
5. Subjects are priorized
regarding their expected
business value and
accessibility
3. Business Experts, Digital & Data
Specialist brainstorm and work together to
bring out insights
PoCs +
Roadmap
{Output}
{Input}
Data +
Strategy
.CODESIGN .SELECTION
.COMBINATION
.ENRICHMENT
.PRIORIZATION.DISCOVERY.SYNCHRO
.SHARE
1. Synchronization of
stakeholders on the
context, business
challenges and AS-IS.
2. Presentation of
technology innovations,
uses cases and REX in
several fields and sectors 4. Ideas and insights are challenged,
reshaped and qualified (feasibility,
workload, approach to validate the value…)
Big Data roadmap definition phase

#CWIN17
How to address a use case

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Electrolysis process: the role of the carbon anodes

#CWIN17
Predictive detection of anode spikes
Issue: periodic spikes crisis leading to production loss and carbon overcosts
Objective:
• Sustain productivity
 Up to 1000$ per spike
• Avoid crisis runaway
 Avg 3% spikes  Crisis: 8%+
Work done: Generating a robust alert
Balance between:
• Detection: 50%
• Anticipation: 8 days
• Precision: 90%
Anode data (new sensors) Hidden Markov Model
Algorithm (HMM)
alerts
Results:
Spikes(%)

#CWIN17
Annual Cash flow savings and Business Case
Financial for Dunkirk smelter:
 average cost of spikes crises: $2 500 000/year
 potential savings with 8 day anticipation: $1 000 000/year
Full potential savings for all Rio Tinto aluminium sites:
Requires:
• to package the algorithm in a “Predictive Function”
• a platform to deploy this function on all plants in real-time operation.
Dunkirk All sites
Savings/year $1 000 000 $12 000 000
IRR 126% 112%
NPV $5 000 000 $56 000 000

#CWIN17
Predictive functions in real time operation
Plant Process Data Analytics (PPDA)
• The PPDA module is the “container” for predictive functions
• Data connectors with the production systems
• Light embedded Hadoop infra for data preparation and predictive
motor execution
• Predictive alerts are displayed in operators electronic shift logs
• Algorithm performance is continuously monitored
Thanks to our standardized Manufacturing Execution System !

#CWIN17
How to accelerate 4.0 roadmap with Open Innovation
Leveraging external funding and cooperative R&D
• Reduce R&D costs
• Accelerate “Time to Market”
• Increase revenues via the development of disruptive solutions
Open Innovation process
Past 2 years?
4 European projects won
15 M€ of assets for only 3 M€ direct funding
 A powerful lever effect ! X 5
Roadmaps:
plants & strategy
Challenges not
covered
Best external
partners
Best funding
opportunity

#CWIN17
Horizon 2020 project: Monsoon
European Commission funding program
SPIRE Sustain Process Industry through Resource & Energy Efficiency
Topic addressed: Plant-wide monitoring and control of data-intensive processes.
• Build some Predictive Functions based on Big Data tools to optimize the production in process
industries
• Develop a plant operations platform to deploy these Predictive Functions in real-time
Our consortium is made of 11 partners from 7 EU countries: ISMB, Rio Tinto (AP), Capgemini,
ProBayes, Fraunhofer, GLN, CERTH, KIMW, TUK, AENOR, LCEN

#CWIN17
Main demo plant: Aluminium Dunkerque
Aluminium Dunkerque is the highest-producing aluminium plant in the EU-28.
One of the most modern smelters:
264 electrolytic pots operating at 380 kA,
yearly producing 275 000 tons of aluminium,
and consumes 3.7 TWh of electricity
… equivalent to a 1-million people city consumption.
Objective:
Our first 4.0 plant !

#CWIN17
Conclusion
Build a 4.0 vision with business cases
Start with some high value creation use cases
Find the good partners
Accelerate with Open Innovation
Have a dedicated demo plant

#CWIN17
Thank You!
Nicolas Monnet
Rio Tinto
MESAL™ Product Manager
nicolas.monnet@riotinto.com
725 rue Aristide Bergès – BP 7,
Voreppe, 38340, France
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contact
picture
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Nathalie Praizelin
Capgemini
MES and Digital Manufacturing Consultant Expert
nathalie.praizelin@capgemini.com
95 chemin de l‘Etoile
38330 - Montbonnot Saint-Martin, France

CWIN17 Toulouse / Industrial big data and mes, the winning combination to improve industrial performance n.praizelin-n.monnet-v2

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CWIN17 Toulouse / Industrial big data and mes, the winning combination to improve industrial performance n.praizelin-n.monnet-v2