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Understanding and steering of metallurgical processes
Dr. Sander Arnout – InsPyro Inspiration Day 4/12/2015

Merge into one operating framework
InsPyro’s vision on knowledge
 Two types of knowledge
2
 Essential to run a process
 Control often depends on individual
 Changes by trial and error
 Mechanisms unclear
 Experience transfer is difficult
 Essential to be in control
 Control depends on model
 Changes are based on physics
 Mechanisms are explicit(ly assumed)
 Transferrable
Experience:
knowledge on how
to run a process
Insight: understanding
the science of a
process

Process development approach
 Stepwise process of increasing knowledge and experience:
1. Idea (opinion)
2. Concept from literature or experience
3. Process model to define expected working area
4. Economic evaluation
5. Lab or pilot scale experiments
6. Validate process model, benchmarking
7. Scale-up or adjustments
 Innovation isn’t random but a structured approach, learning from failures
 Fact-based decision on the road ahead
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Process improvement = development
 Lots of innovation happen on existing facilities
 Increased energy efficiency
 Increase of input from secondary streams
 Increased complexity
 Lots of potential in using existing processes optimally
 Nobody develops a process without a model, yet several processes are
run without an explicit model
 Use the same innovation approach:
 Learn from process behaviour, history, trends, including mistakes and gut feeling
 Build on laws of physics to structure the chaos and avoid relying on opinions
4

Survey on data use in metallurgy
 Level of data management in the organization
5
0% 10% 20% 30% 40% 50% 60%
Data collection is minimal
We collect lots of data but don't use it much
We use data for analysis but it requires a lot of effort
Data analysis is easy but it is difficult to draw conclusions
Data and analysis provide input for decisions with some effort
Data and analysis provide input for decisions in a structured and
automated way

Survey: data use
6
We lose time combining and cleaning data
Yes
Maybe
No
All kinds of data are stored in the same
format or location
We have good tools for
visualization
Some important data is only
stored on paper
Some important data is not
collected
NO
NO
YES
MAYBE

Survey: process
7
There is a lot of fluctuation without a clear
cause
Yes
Maybe
No
Process understanding is mainly
in the brain of the people
We stick to known recipes to
avoid problems
The process results can be
predicted
The process is regarded
as a black box
MAYBE
YES
YES
MAYBE
NO
YES

Metallurgy & Business Intelligence
 ProOpt combines metallurgical insight with data management
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ProOpt goal: increase value creation
9
World Class optimisation and control system for the
process, melting and mining industry
 Info.base: data information system
 Secure availability and quality of data when
you need it
 Reporting.base:
 KPI’s, process and economical information
available at your finger tips
 Model.base:
 Process optimisation based on dynamic
modelling and statistical analysis – measure,
monitor and optimise your process
 Remote control room:
 Updated Experts available online
ProOpt
Remote Control
Room
ProOpt
Model.base
ProOpt
Info.base
ProOpt
Reporting.base
ProOpt
Control System

Expected impact of ProOpt system
 Engineers spend time on making improvements
– not on finding and checking the data
 Optimize feed mix to reduce fluctuation in
process and cost per produced unit
 Better understanding of process reduces
mistakes – makes complex plants manageable
 Wide insight in critical factors – also by
operators, management, purchasing
 Feed forward function reduces critical
happenings
 Go beyond insight and optimise value
10
Value creation
Numbers Information Analysis Fact based
decisions
Management
Purchasing
R&D and Engineering
Operation

ProOpt International: contact details
Lausanne office Leuven office
Avenue de Sevelin 6B Kapeldreef 60
Lausanne 1007 3001 Leuven
Switzerland Belgium
www.ProOpt.net
info@ProOpt.net
Dr. Sander Arnout
sander.arnout@proopt.net
+32 16 298 491
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This presentation was part of the seminar
Data Management and
Fact-Based Decision Making
in Metallurgical Operations
4th of December 2015 – Leuven, Belgium

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Understanding and steering of metallurgical processes

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Understanding and steering of metallurgical processes

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