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Smart Industrial Concept! Design- and Operational Optimization
Rene Hofmann Scientific Coordinator SIC!
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Worldwide changing energy system Transformation of the energy supply in industrial systems
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Challenges − Diversification of energy production − Load flexibilization/sanitization − Volatility of renewable energy sources − Consideration of the energy market
Energy-intensive industry − energy supply system − Optimal plant operation → exploit full potential of industrial plants − Need for flexible designs and predictive automation/control concepts
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Digital Transformation of the Industrial Energy Supply
Cooperative Doctoral School: SIC! [Smart Industrial Concept!]-
Holistic Approach with Digitalization of Industrial Processes and Applications for 2050 and beyond
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Optimum design of the energy supply
and process demand
Operational optimization (CHP, P2X, TES, HTHP,
etc.)
Power market generation
decentralized/ volatile Sector
coupling
Data handling and treatment
SIC! in a Nutshell Added value through specific
use of data
Development of methods for energy-optimized operation of industrial plants
Optimum system design for future environment
Consideration of mutual interaction industry ↔ energy networks
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Research Competences
Power market Sector
coupling
Operational optimization
Design optimization and planning
Data handling and
treatment Process Analysis and Integration
Data Driven Modeling
Mathematical Optimization
Sector Coupling, Power Grids,
Markets
Control Development,
MPC
Thermodynamic System Modeling
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SIC! [Smart Industrial Concept] https://sic.tuwien.ac.at
Power market sector coupling Operational optimization Design optimization and
planning Data handling and
treatment
PhD#1
PhD#2
PhD#4
PhD#8
PhD#6
PhD#7
PhD#3
PhD#5
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Experienced industrial partners…
...supported by scientific excellence 7
SIC! united and well balanced approach
CONSULTANT MARKET
OPERATOR
IMPLEMENTERS
R&D
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Operational optimization
PhD#3: Operational optimization concepts with integration of storage systems for load flexibilization in the energy-intensive industry
PhD#5: Development of methods to optimally control the supply of energy-intensive industrial processes by integrating waste heat and using components to increase load flexibility
SG
EBTES
Heat Demand
Energy Price
Energy flow
Information flow
Optimization
SG: Steam Generator EB: Electrode Boiler TES: Thermal Energy Storage
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Intelligent Design/Operational Optimization
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Energy Supply Units
Supply Demand
Source: R. Hofmann, S. Dusek, M. Koller, H. Walter: "Flexibilisierungspotenzial für Energieanlagen in der Industrie. Intelligentes Demand-Side-Management durch Integration von thermischen Speichern - Teil 1"; BWK, 68 (2016), 9; 6 – 11.
Simple Example
≠ ?
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SG + TES + EB
Supply Demand
Source: R. Hofmann, S. Dusek, M. Koller, H. Walter: "Flexibilisierungspotenzial für Energieanlagen in der Industrie. Intelligentes Demand-Side-Management durch Integration von thermischen Speichern - Teil 1"; BWK, 68 (2016), 9; 6 – 11.
Results
=
SG: Steam Generator EB: Electrode Boiler TES: Thermal Energy Storage
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Source: R. Hofmann, S. Dusek, M. Koller, H. Walter: "Flexibilisierungspotenzial für Energieanlagen in der Industrie. Intelligentes Demand-Side-Management durch Integration von thermischen Speichern - Teil 1"; BWK, 68 (2016), 9; 6 – 11.
Results
Energy Market-Prices
SG: Steam Generator EB: Electrode Boiler TES: Thermal Energy Storage
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Source: R. Hofmann, S. Dusek, M. Koller, H. Walter: "Flexibilisierungspotenzial für Energieanlagen in der Industrie. Intelligentes Demand-Side-Management durch Integration von thermischen Speichern - Teil 1"; BWK, 68 (2016), 9; 6 – 11.
Results
States (SG +TES + EB)
SG: Steam Generator EB: Electrode Boiler TES: Thermal Energy Storage
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Potential depending on individual Process
Result of a test study: o fictitious energy supply system o historical electricity prices o constant biomass price o simulation time: 1 week
0,00%
7,05%
13,87%
20,69%
nur DE TES EK TES + EK0%
5%
10%
15%
20%
25%Saving Potentials Fuel costs / Electricity
costs in %
SG only TES EB TES+EB
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Motivation… PI system beneficial for research activities:
single point of truth
preparation of data for data scientists
cross border enablement
“Unified data from different sources to provide data scientists the same view at the same time step…”
Data handling
and treatment
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Catalog
Glossary/Thesaurus
Taxonomy
Semantic network
Ontology
Information Models
Text/HTML
XML
RDF
RDFS
OWL Semantic Interoperability
Structural Interoperability
Syntactic Interoperability
high semantics
weak semantics
low complexity high complexity
SIC!
Knowledge Representation: Expressiveness
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SIC! Ontology
Plant Design
Control Design Model
Design
SIC! Runtime
Data Mining
Data Analysis
Model Transformation
Model Tuning
Knowledge Representation: Design/Runtime
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Sensors
Millions of Smart Devices
PDC/Edge OSIsoft
Cloud Services Classic PI System
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SIC! Runtime System
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Build / Train Models
OSIsoft PI System PI Data Archive ( time series )
PI Asset Framework Hosting Common Asset
Model ( Assets, Event Frames)
Plant
OPC UA
MPC evon
Other Formats
User User
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Thermal Energy Storage Model
1D Model +
Model validation with real Lab measurement data
Source: M. Koller, R. Hofmann, Mixed Integer Linear Programming Formulation for Sensible Thermal Energy Storages, Proceedings of the 28th ESCAPE, Graz 2018.
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Validation of the 1D model of the fixed bed regenerator Highly dynamic operation Temperature spread
Source: F. Mayrhuber, H. Walter, M. Hameter, 2017. Experimental and numerical investigation on a fixed bed regenerator. 10th International Conference on Sustainable Energy and Environmental Protection.
Model Experiment
Comparison Experiment - Simulation
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Implementation of OSIsoft PI System PI Data Archive (time series) PI Asset Framework to the fixed bed regenerator at the TU Wien lab.
Connection via evon – XAMControl® automation system
Next Steps…
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Physical vs. data driven Analysis of model
formulation (for exact description of storage behavior)
Neuronal network techniques Full understanding of the
highly dynamic operation
Experiment
050
100150200250300350400
0 500 1000 1500
Data-Model
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Model Comparison
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Acknowledgements To the numerous contributors of this presentation W. Kastner,
PhD-candidates: S. Panuschka, A. Beck, M. Koller, C. Seykora, B. Pesendorfer.
To AIT-TU Wien partnership with the joint professorship of Industrial Energy Systems
To all partners and supporters of the Cooperative Doctoral School SIC!
To OSI partnership and setup suggestions (Frank Batke)
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This Presentation is based on Publications o Keynote-Lecture: Beck, A. and Hofmann. R: “Extensions for Multi-Period MINLP Superstructure
Formulation for Integration of Thermal Energy Storages in Industrial Processes”, in Proceedings of the 28th European Symposium on Computer Aided Process Engineering, June 10th to 13th, 2018, Graz, Austria. © 2018 Elsevier B.V. http://dx.doi.org/10.1016/B978-0-444-64235-6.50234-5, pp 1335-1340.
o Koller, M. and Hofmann. R: “Mixed Integer Linear Programming Formulation for Sensible Thermal Energy Storages”, in Proceedings of the 28th European Symposium on Computer Aided Process Engineering, June 10th to 13th, 2018, Graz, Austria. © 2018 Elsevier B.V. http://dx.doi.org/10.1016/B978-0-444-64235-6.50163-7, pp 925-930.
o R. Hofmann, S. Dusek, M. Koller, H. Walter: "Flexibilisierungspotenzial für Energieanlagen in der Industrie. Intelligentes Demand-Side-Management durch Integration von thermischen Speichern - Teil 1"; BWK, 68 (2016), 9; 6 – 11.
o F. Mayrhuber, H. Walter, M. Hameter, 2017. Experimental and numerical investigation on a fixed bed regenerator. 10th International Conf. on Sustainable Energy and Environmental Protection.
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SIC! [Smart Industrial Concept]
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Univ.Prof. Dr. René Hofmann Scientific Coordinator SIC! TU Wien – Institute of Energy Systems and Thermodynamics AIT Austrian Institute of Technology [email protected] [email protected]
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