The importance of thermodynamics for business intelligence … · 2018-03-19 · InsPyro –...
Transcript of The importance of thermodynamics for business intelligence … · 2018-03-19 · InsPyro –...
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The importance of thermodynamics for business intelligence tools
Dr. Sander Arnout (InsPyro) – ProOpt International
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InsPyro – inspiring metallurgy
� KU Leuven spin-off, est. 2009
� High-temperature processes
� Slag, refractory, off-gas
� Furnace modelling and steering
� Classical and novel technology
� Consultancy in metallurgy
� Research projects in waste
� Software for metallurgicalcalculations on site
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ProOpt International SA
� Experts available in
� Metal industry
� Mining industry
� Data management
� Engineering
� Process management
� Market & Finance
� ProOpt Optimisation
� Data integration
� Process modelling
� KPI Reporting
� Process optimisation
� Value optimisation
� Knowledge sharing
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& &
have pooled their knowhow and systems to offer
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Founding companies of ProOpt
� InsPyro
� University spin-off company, founded 2009
� Technical consultancy in steel and non-ferrous industry
� Spark software for predefined models and thermodynamic calculations
� Proval Partners
� Experience in trading, market, finance
� Acquired ErasMetal in 2011 and turned it around
� Reliable data and modelling proved key in turning the plant around
� bee.solutions
� Data management experts
� Experience in oil industry, financial institutions, telecom…
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Metallurgy & Business Intelligence
� ProOpt combines metallurgical insight with data management
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4 steps to optimize metallurgical process and profitability
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1. You trust your numbers
2. Numbers becomes information
3. Information becomes analysis
4. Analysis leads to fact based decisions
Moving up the ladder increases returns� Better process understanding
� Less deviations/unexpected events
� More efficient operations
� Lower cost structure
� Reduction of operational risks
Value creation
Numbers Information Analysis Fact based
decisions
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ProOpt goal: increase value creation
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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
ProOptRemote Control
Room
ProOptModel.base
ProOptInfo.base
ProOptReporting.base
ProOpt
Control System
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Expected impact of ProOpt system
� Engineers spend time on doing the work – not 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
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Value creation
Numbers Information Analysis Fact based
decisions
Management
Purchasing
R&D and Engineering
Operation
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What is the role of thermodynamics?
� Thermodynamics as a framework
� Base assumption for unknown processes
� Non-linear effects based on reaction equilibrium, liquidus,...
� Allowing extrapolation
� Allowing determination of deviation from equilibrium and empirics
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Example: impurity volatilization
� Fitting process data: always noisy, which shape to take?
� Not simple linear behavior:
� 2 element’s vapor pressures
� Metallic and oxidic forms in the gas phase
� Thermodynamics enables more reliable extrapolation
� Not just one variable but the whole process
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0
0.05
0.1
0.15
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0.25
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0.35
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800 850 900 950 1000 1050 1100 1150 1200
imp
uri
ty r
ati
o p
rod
uct
/ba
thTemperature (°C)
Process data
Thermodynamics
Expon. (Process data)
Linear (Process data)
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0
0.1
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800 900 1000 1100 1200 1300 1400
imp
uri
ty r
ati
o p
rod
uct
/ba
th
Temperature (°C)
Process data
Thermodynamics
Expon. (Process data)
Linear (Process data)
Example: impurity volatilization
� Fitting process data: always noisy, which shape to take?
� Not simple linear behavior:
� 2 element’s vapor pressures
� Metallic and oxidic forms in the gas phase
� Thermodynamics enables more reliable extrapolation
� Not just one variable but the whole process
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Example: impurity volatilization
Now if we have these relations for several impurities, we can:
� Select parameters for the existing process
� Determine if we should invest in different temperature process
� Create a “virtual instrument” for the bath temperature:
� From measured impurities, calculate best fit temperature
� Decide on steering to meet specifications (e.g. decrease 50°C)
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Process
Input
composition
Product
composition
Temperature?
Intelligent
feedback
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Example: Zinc fuming
� Classical example of thermodynamics vs. kinetics discussion
� Thermodynamics as “best case”
� Reaction can be incomplete, but cannot go further than equilibrium
� If deviation from equilibrium is relevant, add kinetic or empiric model
� To design a better reactor, need more detailed type of model
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E. Jak et al., 2002
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Eras plant layout
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ArcFume Technology for Reprocessing Residues from Industry, Imris M. Swartling M. Heegaard B. M., EMC 2013
� ScanArc ArcFume technology
� Built in 2005 with unique set-up
� 50 ktpa EAF dust capacity
� Acquired by Proval Partners in 2011
� Sold to Nyrstar in 2014
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Eras plant model
� In 2011, clear need to stabilize and optimize the process
� Reduce standstills = improve throughput
� Start to build systematic mass and energy balance
� Mark W. Kennedy, an authority on slag furnaces, was attracted by Proval
� Full flowsheet model containing charge mixer, furnace, plasma generators, filter,...
� Use of a formal model rather than control by“feeling”
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Advantage of models to learn from data
� Example: explanation of furnace temperature and energy need, depending on the mix
� Correlation statistics: will only give you noise
� AI: may find a link between high temperatures and certain raw materials
� Mass balance & thermodynamics:
� Expected slag composition
� Expected slag melting point – virtual instrument
� Correlation furnace temperature and melting point found
� Unravel mechanism step by step
� Next step: expected furnace temperature from model
� More relevant correlations (=understanding) using known relations
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Eras business development
� Assessment to be made with every offer:
� What is the production cost to treat this material?
� How much zinc oxide will we produce, and with what quality?
� So, in the end, what is the margin, and...
� Will we take this material or not?
� Cooperation between technical and commercial side crucial to detect opportunities
� E.g. batteries or battery fractions
� When compensated correctly, the flexibility of the process was shown to be much larger than previously assumed
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The new life of Eras
� Successful turnaround of the plant:
� Process stable, standstills decreased
� Profitability increased
� Slag useable in building products
� Sold to Nyrstar, to become part of their strategic investments
� The plant will be modified to treat primary zinc byproducts
� Fact-based and model-driven plant management (including thermodynamics) had shown to pay off
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ProOpt goal: increase value creation
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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
ProOptRemote Control
Room
ProOptModel.base
ProOptInfo.base
ProOptReporting.base
ProOpt
Control System
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ProOpt International: contact details
Lausanne office Leuven office
Avenue de Sevelin 6B Kapeldreef 60
Lausanne 1007 3001 Leuven
Switzerland Belgium
www.ProOpt.net
Dr. Sander Arnout
+32 16 298 491
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