Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Modelling and Simulation in Industrial
Applications
Applying Energy Optimization to
Large Scale Systems
DI Matthias Rößler...
Current Situation
• Energy Consumption Austria
• Challenges regarding energy efficiency
– no holistic view on production p...
Energy Optimization -
Motivation
• Energy consumption in production industry
approx. 40% of total energy consumption
in in...
Application Projects
Interdisziplinäre Forschung
zur Energieoptimierung in
Fertigungsbetrieben
(interdisciplinary research...
5
Interdisziplinäre Forschung
zur Energieoptimierung
in Fertigungsbetrieben
(interdisciplinary research
for energy optimiz...
INFO - Approach
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to ac...
INFO
Partial Model: Machines
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilit...
INFO
Partial Model: Machines
• machines
– machine tools
– laser cutters
– ovens
– compressors
• production scenario
– mode...
INFO
Machine Tools
initial approach
• technological
• focus on
modelling
individual tasks
of machine tools
• what is possi...
initial approach
• technological
• focus on
modelling
individual tasks
of machine tools
• what is possible
from the
techno...
INFO
Machine Tools
initial approach
• technological
• focus on
modelling
individual tasks
of machine tools
• what is possi...
INFO
Machine Tools
initial approach
• technological
• focus on
modelling
individual tasks
of machine tools
• what is possi...
INFO
Partial Model: Building
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilit...
INFO – Building III
Output
daylight
dependent
control of
- artificial light
- shading
heat output/
cooling capacity
zone
t...
INFO
Partial Model: Energy System
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production fa...
INFO
Energy System I
Output
required
power
•heat
•electricity
•others
CO2
emissions
Energy System ModelInput
weather data
...
INFO
Energy System II
0
500
1000
1500
10.1. 8.4. 1.8. 10.1. 8.4. 1.8. 10.1. 8.4. 1.8.
Location Cairo
low utilization
high ...
INFO
Overall Simulation
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
....
INFO: Specific Aims
Optimization based on simulation
• increase of energy efficiency
• inclusion of new carriers of energy...
INFO: Approach
Theoretical Modelling Technical Integration
Goal: integrated dynamic simulation
• overall system not implem...
INFO: Overall Simulation
coupling
framework
economic and
ecologic
evaluation
static input
 temperature
 solar radiation
...
INFO – Co-Simulation
• cooperative simulation with control of data exchange via
framework
• individual simulators calculat...
INFO – Co-Simulation
Loose Coupling (Jacobi Type)
System 1
System 2
Jacobi Type:
Model Problem:
Extrapolation of y1 and y2...
INFO – Co-Simulation
Loose Coupling (Gauß-Seidl Type)
Gauß-Seidl Type:
System 1
System 2
Extrapolation of y2
Interpolation...
INFO – Co-Simulation
Consistency
• consistency error measures the error of the
numeric method in one step
• consistency er...
INFO – Co-Simulation
BCVTB I
• Building Controls Virtual Test Bed
• open-source software platform (developed at Lawrence
B...
INFO – Co-Simulation
BCVTB II
• communication via BSD sockets and network protocol
(inter-process communication)
• Loose C...
INFO – Co-Simulation
Simulation control framework
BCVTB
Machine Simulation
MATLAB/Excel
Building Simulation
EnergyPlus
Ene...
INFO - Results
• scenarios for different HVAC systems –
performance prediction
• energy performance certificate
• lifecycl...
30
Balanced
Manufactoring
Software Tool-Chain, embedded in operational automation
systems:
 BaMa-Optimization: optimization of line operation
regar...
BaMa - Approach
• Modularisation of the system „production facility“
 partitioning according to energetic reasons
 separ...
BaMa – Cubes I
 Cubes are clearly confined units  basic modules for system
analysis
 integration of different points of...
modelling hitherto modelling with Cube approach
 equal system boundaries
 modular, expandable and easy to apply to speci...
BaMa
Cubes: Interfaces
 cubes have uniformly defined interfaces
 flexibility, modularising, exchangeability
 connection...
BaMa Toolchain
• Cubes also help with the description in the simulation environment
• Cubes have a virtual „counterpart“ -...
BaMa – Tool Chain
37
BaMa – Cube Classes
„Cube“
machine,
production process
value-adding
non-value-
adding
building
building hull
thermal zone
...
BaMa – DEV&DESS I
39
products
“flows”
products & “flows”
BaMa – DEV&DESS II
Formalism
• building on systems-theoretical basics
• allows the description of hierarchically structure...
BaMa – DEV&DESS III
Cube
guarantees
 consistency in the cube description
 technical feasibility
 requirements for susta...
Real Cube
Model
(verbal, conceptual, physical, mathematical)
Formal Cube Description
DEV&DESS Formulation of the Cube
DEV&...
Real Cube
BaMa – Cube Workflow II
43
Model
(verbal, conceptual, physical, mathematical)
BaMa – Cube Workflow III
44
Formal Cube Description
BaMa – Cube Workflow IV
45
Formal Cube Description
...
Bedarf el. Leistung (PelB)
Anforderung Entität (Ereq)
Elektrische Leistung (Pel)
Entität (E)
E...
DEV&DESS Formulation of the Cube
Name Kürzel Einheit Datentyp Wertebereich
Entität E Entität
Attribut: Masse E.m kg Skalar...
DEV&DESS Formulation of the Cube
Ausgang
• wird nur bei Beendigung des Betriebszustands "halten"
ausgegeben
• Unterscheidu...
DEV&DESS-Implementation of the Cube
= virtual Cube
BaMa
Cube Workflow VIII
49
BaMa - Optimization
• scenario: production plans, operational conditions
(constraints, initial solution)
• optimization se...
BaMa - Optimization
Target Function
• weighing of different criteria:
– on-time delivery, storage
– total energy cost
– th...
production
BaMa - Carbon Footprint of
Products (CFP)
 evaluation of environmental sustainability of a product throughout ...
CFP from
heating/cooling of
storerooms
BaMa - CFP Method
exemplary tasks at an up-to-date CFP calculation
consideration of...
BaMa - Results
• modular approach for high flexibility
• carbon footprint of products
• automated optimization of producti...
Conclusion
• energy efficiency: increasing need for simulation
based solutions
• two different approaches
– co-simulation
...
THANK YOU FOR YOUR ATTENTION!
Upcoming SlideShare
Loading in …5
×

Roessler, Hafner - Modelling and Simulation in Industrial Applications: Applying energy optimization to large scale systems

250 views

Published on

Workshop at Universtiy of Applied Science Vienna - 'Data Science in Industry 4.0 and IoT'

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Roessler, Hafner - Modelling and Simulation in Industrial Applications: Applying energy optimization to large scale systems

  1. 1. Modelling and Simulation in Industrial Applications Applying Energy Optimization to Large Scale Systems DI Matthias Rößler DI Irene Hafner dwh simulation services
  2. 2. Current Situation • Energy Consumption Austria • Challenges regarding energy efficiency – no holistic view on production process with respect to resource consumption – highly complex matter – lack of expertise on energy systems in enterprises – lack of knowledge of possibilities 2.1% 28.7% 32.9% 12.4% 23.9% Agriculture Manufacturing Transport Service Sector Private Households Statistik Austria, 2011 2
  3. 3. Energy Optimization - Motivation • Energy consumption in production industry approx. 40% of total energy consumption in industrialized nations • Potential for reduction: 30-65% (depending on sector)  Increase of energy costs  Tougher regulations  Rising ecological awareness Importance of energy efficiency in the industrial sector 3
  4. 4. Application Projects Interdisziplinäre Forschung zur Energieoptimierung in Fertigungsbetrieben (interdisciplinary research for energy optimization in production facilities) Balanced Manufactoring 4
  5. 5. 5 Interdisziplinäre Forschung zur Energieoptimierung in Fertigungsbetrieben (interdisciplinary research for energy optimization in production facilities)
  6. 6. INFO - Approach Analysis Modelling Integrated Simulation ... aiming energy optimization in production facilities ... to achieve economic and ecologic goals Optimization Fields of Optimization Energy System Production System MachineProcess Building 6
  7. 7. INFO Partial Model: Machines Analysis Modelling Integrated Simulation ... aiming energy optimization in production facilities ... to achieve economic and ecologic goals Optimization Fields of Optimization Energy System Production System MachineProcess Building 7
  8. 8. INFO Partial Model: Machines • machines – machine tools – laser cutters – ovens – compressors • production scenario – modelling a load profile via SAP data of a representative production week • considered energy flows (3 thermal zones) – electric – diffuse heat emission – recoverable heat 8
  9. 9. INFO Machine Tools initial approach • technological • focus on modelling individual tasks of machine tools • what is possible from the technological point of view? Step Back and focus on 15 minute average values • approach from the opposite direction • which values are required to generate the desired output? data based machine model • model based on measured data • easily parameterized • modular built, hence flexible • available production data from respective enterprise are essential 9 measurements •AMS (Stiwa): Hermle C40, C32 •Anger Machining: HCX BA 1035, HCX BA 1110 •CNC Profi (DMG): DMU 65 •EMCO: Maxxturn 45 •Krause Mauser: Invers BAZ for Daimler •Hoerbiger: Stama/MC 334 Twin
  10. 10. initial approach • technological • focus on modelling individual tasks of machine tools • what is possible from the technological point of view? Step Back and focus on 15 minute average values • approach from the opposite direction • which values are required to generate the desired output? data based machine model • model based on measured data • easily parameterized • modular built, hence flexible • available production data from respective enterprise are essential measurements •AMS (Stiwa): Hermle C40, C32 •Anger Machining: HCX BA 1035, HCX BA 1110 •CNC Profi (DMG): DMU 65 •EMCO: Maxxturn 45 •Krause Mauser: Invers BAZ for Daimler •Hoerbiger: Stama/MC 334 Twin INFO Machine Tools 3,000 5,000 7,000 9,000 11,000 13,000 468 470 472 474 476 478 480 482 484 El.Power[W] Zeit [s] Leistung ohne Werkstück Leistung mit Werkstück slowing-down process of the approaching cutting unit approaching the workpiece without tool usage tool usage (drilling) tool usage (finish drilling) drill move out and approach tothe next drilling short move out of the drill (ejection of chippings) Power without workpiece Power with workpiece 10
  11. 11. INFO Machine Tools initial approach • technological • focus on modelling individual tasks of machine tools • what is possible from the technological point of view? Step Back and focus on 15 minute average values • approach from the opposite direction • which values are required to generate the desired output? data based machine model • model based on measured data • easily parameterized • modular built, hence flexible • available production data from respective enterprise are essential measurements •AMS (Stiwa): Hermle C40, C32 •Anger Machining: HCX BA 1035, HCX BA 1110 •CNC Profi (DMG): DMU 65 •EMCO: Maxxturn 45 •Krause Mauser: Invers BAZ for Daimler •Hoerbiger: Stama/MC 334 Twin location building production chain machine process T O P D O W N B O T T O M U P compressor model machine tool model physical background and measurement oven and laser model 11 0 50 100 150 200 Mon Tue Wed Thu Fri Sat Sun Mon elektrischeLeistunginkW Maschinenpark Shedhalle Kompressorencompressorsmachines electricpowerinkW
  12. 12. INFO Machine Tools initial approach • technological • focus on modelling individual tasks of machine tools • what is possible from the technological point of view? Step Back and focus on 15 minute average values • approach from the opposite direction • which values are required to generate the desired output? data based machine model • model based on measured data • easily parameterized • modular built, hence flexible • available production data from respective enterprise are essential measurements •AMS (Stiwa): Hermle C40, C32 •Anger Machining: HCX BA 1035, HCX BA 1110 •CNC Profi (DMG): DMU 65 •EMCO: Maxxturn 45 •Krause Mauser: Invers BAZ for Daimler •Hoerbiger: Stama/MC 334 Twin 0 20 40 60 80 100 120 140 160 180 200 Mon Tue Wed Thu Fri Sat Sun Mon electricpowerinkW Messung Modell • 25 machine tools in the production hall • comparison measurement/model measurement model 12
  13. 13. INFO Partial Model: Building Analysis Modelling Integrated Simulation ... aiming energy optimization in production facilities ... to achieve economic and ecologic goals Optimization Fields of Optimization Energy System Production System MachineProcess Building 13
  14. 14. INFO – Building III Output daylight dependent control of - artificial light - shading heat output/ cooling capacity zone temperature Building ModelInput weather data waste heat people/ devices waste heat machines 14
  15. 15. INFO Partial Model: Energy System Analysis Modelling Integrated Simulation ... aiming energy optimization in production facilities ... to achieve economic and ecologic goals Optimization Fields of Optimization Energy System Production System MachineProcess Building 15
  16. 16. INFO Energy System I Output required power •heat •electricity •others CO2 emissions Energy System ModelInput weather data heat output/ cooling capacity zone temperatures waste heat machines recoverable CO2 16
  17. 17. INFO Energy System II 0 500 1000 1500 10.1. 8.4. 1.8. 10.1. 8.4. 1.8. 10.1. 8.4. 1.8. Location Cairo low utilization high utilization medium utilization 0 500 1000 1500 10.1. 8.4. 1.8. 10.1. 8.4. 1.8. 10.1. 8.4. 1.8. EnergyDemand[kWh] Location Vienna low utilization medium utilization high utilization 0 500 1000 1500 2000 2500 10.1. 8.4. 1.8. 10.1. 8.4. 1.8. 10.1. 8.4. 1.8. Location Moscow high utilization medium utilization low utilization Scenario 1: oil heating compression chiller Scenario 2: Heat pump absorption chiller with heat recovery Scenario 2 heat (cooling) Scenario 2 electricity (heating) Scenario 1 heat (heating) Scenario 1 electricity (cooling) 17
  18. 18. INFO Overall Simulation Analysis Modelling Integrated Simulation ... aiming energy optimization in production facilities ... to achieve economic and ecologic goals Optimization Fields of Optimization Energy System Production System MachineProcess Building 18
  19. 19. INFO: Specific Aims Optimization based on simulation • increase of energy efficiency • inclusion of new carriers of energy • manual comparison of specific scenarios • no automatic optimization Formalization of the model structure – reference model • independent of specific implementation and simulation environment component based black-box approach, modularization • illustration of dynamic dependencies and feedbacks connection of variables and interface definition • integration of planning and simulation 19
  20. 20. INFO: Approach Theoretical Modelling Technical Integration Goal: integrated dynamic simulation • overall system not implementable in one simulator – different modelling approaches – gravely differing dynamics (time constants) • several fields of expertise • dynamic coupling Coupling of well-established simulation tools Co-Simulation 20
  21. 21. INFO: Overall Simulation coupling framework economic and ecologic evaluation static input  temperature  solar radiation  waste heat of people and devices  electricity consumption of devices  energy consumption  CO2 emission machine model building model energy system model  e.g. waste heat reuseable/diffuse  electricity consumption of machines  weather data  diffuse waste heat machines  waste heat of people and devices  room temperatures  air change rate  heating and cooling demands  room temperatures  air change rate  heating and cooling demands  reuseable waste heat of machines  energy consumption  CO2 emission 21
  22. 22. INFO – Co-Simulation • cooperative simulation with control of data exchange via framework • individual simulators calculate system parts independently – different solver algorithms – different time steps • data exchange between simulators via framework at previously defined points in time • different ways of data exchange – Strong Coupling: iterative data exchange in every step – Loose Coupling: extrapolation between synchronization references required … 22
  23. 23. INFO – Co-Simulation Loose Coupling (Jacobi Type) System 1 System 2 Jacobi Type: Model Problem: Extrapolation of y1 and y2 23 System 1: System 2:
  24. 24. INFO – Co-Simulation Loose Coupling (Gauß-Seidl Type) Gauß-Seidl Type: System 1 System 2 Extrapolation of y2 Interpolation of y1 24 Model Problem: System 1: System 2:
  25. 25. INFO – Co-Simulation Consistency • consistency error measures the error of the numeric method in one step • consistency error in loose coupling co-simulation: • ODE solver of first order: consistency order maintained • solver of higher order: lower consistency order … consistency error of the method in a mono-simulation … Lipschitz constant of the “right side“ from … coefficient from the second characteristic polynomial 25
  26. 26. INFO – Co-Simulation BCVTB I • Building Controls Virtual Test Bed • open-source software platform (developed at Lawrence Berkeley National Laboratory, University of California) • middleware for run-time coupling of different simulation environments • software components (clients) are executed in parallel 26
  27. 27. INFO – Co-Simulation BCVTB II • communication via BSD sockets and network protocol (inter-process communication) • Loose Coupling (Jacobi Type) with equidistant time steps • in INFO: combination of – MATLAB: data-based models – EnergyPlus: thermal building simulation – Dymola: component-based modelling of technical equipment 27
  28. 28. INFO – Co-Simulation Simulation control framework BCVTB Machine Simulation MATLAB/Excel Building Simulation EnergyPlus Energy System Simulation Dymola Post - Processing MATLAB 28
  29. 29. INFO - Results • scenarios for different HVAC systems – performance prediction • energy performance certificate • lifecycle cost-benefit analysis • roadmap for energy efficient production Energy Efficient Production 29
  30. 30. 30 Balanced Manufactoring
  31. 31. Software Tool-Chain, embedded in operational automation systems:  BaMa-Optimization: optimization of line operation regarding the goals energy, time, costs, quality  optimized operational management strategy  identification of main potential savings  BaMa-Prediction: prediction of energy demands of the whole facility based on production plan, operational management and prediction data  BaMa-Monitoring: aggregation and visualisation of resource demands BaMa - Goals 31
  32. 32. BaMa - Approach • Modularisation of the system „production facility“  partitioning according to energetic reasons  separation into manageable parts  systematically approaching the high system complexity  modular approach allows flexibility • consistent terminus: „cube“ 32
  33. 33. BaMa – Cubes I  Cubes are clearly confined units  basic modules for system analysis  integration of different points of view and system areas (machines, building services, building, logistics) in one system  general Cube specification  Cubes bundle information and resource flows (energy, material, costs, etc.) within identical balance borders  transparency und analysis of energy flows  new modular technology allows optimal connection of the real and the virtual system real production facility machine building services building logistics energy, material and information flow 33
  34. 34. modelling hitherto modelling with Cube approach  equal system boundaries  modular, expandable and easy to apply to special areas in practise  concurrent consideration of energy flows and material flows in one system  overlapping/non-equal system boundaries, hence redundancies  different models for energy flow, material flow and costs  concurrent consideration of flows not possible BaMa – Cubes II 34 Mass balance Energy balance Time balance Cost balance production machine production machine air compressor waste disposal production process Cube production machine Cube production machine Cube air compressor Cube waste disposal Mass balance Energy balance Time balance Cost balance production process information and resource flow
  35. 35. BaMa Cubes: Interfaces  cubes have uniformly defined interfaces  flexibility, modularising, exchangeability  connections and interactions between cubes  material flow  energy flow  information flow  diffuse waste heat, recoverable heat  CO2 share  balance equations at cube borders monitoringdata controlaction energyflow energyflow  work piece, baking goods, etc.  discretized  footprint (costs, CO2) material flow material flow parameters:  dimensions  power characteristics  efficiency  etc.  production plan  operating mode  control signal  etc.  energy demand  operational state  etc.  power: electric, thermal, etc.  exergy measure  CO2 share  work piece, baking goods, etc.  updated footprint 35
  36. 36. BaMa Toolchain • Cubes also help with the description in the simulation environment • Cubes have a virtual „counterpart“ - based on simulation models and measured data • Cube view supports reusability in implementation control status User Interface BaMa - Virtual Cubes real production facility machine building services building logistics energy, material and information flow virtual system Virtual cube machine Virtual cube building services Virtual cube building Virtual cube logistics information flow 36
  37. 37. BaMa – Tool Chain 37
  38. 38. BaMa – Cube Classes „Cube“ machine, production process value-adding non-value- adding building building hull thermal zone energy system, building services energy converter energy storage energy networks logistics transport system handling system storage system 38
  39. 39. BaMa – DEV&DESS I 39 products “flows” products & “flows”
  40. 40. BaMa – DEV&DESS II Formalism • building on systems-theoretical basics • allows the description of hierarchically structured systems • DEVS: description of purely event based (and hence time-discrete) systems • DESS: description of causal continuous systems • DEV&DESS: suitable for hybrid systems supporting continuous as well as discrete changes in system states Implementation • event scheduling required • zero-crossing detection for(real) State Events desired • numerical solving of differential equations can be realised in the model • data models can be included 40
  41. 41. BaMa – DEV&DESS III Cube guarantees  consistency in the cube description  technical feasibility  requirements for sustainable implementation  scientific acceptance 41
  42. 42. Real Cube Model (verbal, conceptual, physical, mathematical) Formal Cube Description DEV&DESS Formulation of the Cube DEV&DESS Implementation of the Cube = virtual Cube BaMa – Cube Workflow I 42
  43. 43. Real Cube BaMa – Cube Workflow II 43
  44. 44. Model (verbal, conceptual, physical, mathematical) BaMa – Cube Workflow III 44
  45. 45. Formal Cube Description BaMa – Cube Workflow IV 45
  46. 46. Formal Cube Description ... Bedarf el. Leistung (PelB) Anforderung Entität (Ereq) Elektrische Leistung (Pel) Entität (E) Entität(E) Abfall (EA) Umgebungstemperatur (Tu) Nicht nutzbare Abwärme (QAW) Nutzbare Abwärme (Qrec) Produktionsplan (Pplan) Heizleistung (PH) Haltedauer (tB) Solltemperatur (Tsoll) Zweipunktregler Hysterese (H) Volumen Ofen (V) Wärmedurchgang Ofenwand (UA) Wärmekapazität Luft (cpL) Dichte Luft (rhoL) Abwärmenutzung (eta) Abfallmenge (alpha) Parameter: Zustandsgrößen: Betriebszustand (p): standby, aufheizen, warten, halten Heizzustand (h): on, off Masse der Entität im Ofen (m) Wärmekap. der Entität im Ofen (cp) Temperatur im Ofen (T) BaMa – Cube Workflow V 46
  47. 47. DEV&DESS Formulation of the Cube Name Kürzel Einheit Datentyp Wertebereich Entität E Entität Attribut: Masse E.m kg Skalar > 0 Attribut: Temperatur E.T K Skalar > 0 Attribut: Wärmekap. E.cp J/(kg*K) Skalar > 0 Name Kürzel Einheit Datentyp Wertebereich Entität E Entität Attribut: Masse E.m kg Skalar > 0 Attribut: Temperatur E.T K Skalar > 0 Attribut: Wärmekap. E.cp J/(kg*K) Skalar > 0 Abfall EA Entität Attribut: Masse EA.m kg Skalar > 0 Attribut: Temperatur EA.T K Skalar > 0 Attribut: Wärmekap. EA.cp J/(kg*K) Skalar > 0 Materialflüsse Eingänge: Ausgänge: BaMa – Cube Workflow VI 47
  48. 48. DEV&DESS Formulation of the Cube Ausgang • wird nur bei Beendigung des Betriebszustands "halten" ausgegeben • Unterscheidung: Entstehung von Abfall BaMa Cube Workflow VII 48
  49. 49. DEV&DESS-Implementation of the Cube = virtual Cube BaMa Cube Workflow VIII 49
  50. 50. BaMa - Optimization • scenario: production plans, operational conditions (constraints, initial solution) • optimization selects control variables (production plan) • target function: evaluating the current simulation results for the chosen parameters • selection of new parameters for next simulation run • iteration to find the most suitable production plan for the respective scenario within a given time span Scenario control variables optimization target function parameters feedback modified parameters Simulation 50
  51. 51. BaMa - Optimization Target Function • weighing of different criteria: – on-time delivery, storage – total energy cost – throughput time – idle period – … delayed delivery, storage costs (on-time delivery) total throughput time total energy: costs – CO2 total number: DESIRED - ACTUAL lot throughput weights (adjustable) 51
  52. 52. production BaMa - Carbon Footprint of Products (CFP)  evaluation of environmental sustainability of a product throughout its whole life cycle  comparison to other products  identification of pollution during life cycle  reduction of pollutant emissions CO2-footprint of a product resources utilization disposal 52
  53. 53. CFP from heating/cooling of storerooms BaMa - CFP Method exemplary tasks at an up-to-date CFP calculation consideration of stand-by and setup times energy for building services energy input of machines apportioned to machines energy for transport systems ventilation, illumination,… of the building 53
  54. 54. BaMa - Results • modular approach for high flexibility • carbon footprint of products • automated optimization of production plans • aims: effecitivity regarding – energy – costs – resources – CFP • proof of concept with six use cases in several production facilities from different fields 54
  55. 55. Conclusion • energy efficiency: increasing need for simulation based solutions • two different approaches – co-simulation (quasi) arbitrary amount of participating simulators most suitable software for every partial system individual solvers/time steps for partial systems loss of accuracy – DEV&DESS formalism monolithic approach (one simulator) no accuracy loss need to formalize (adapt model description) 55
  56. 56. THANK YOU FOR YOUR ATTENTION!

×