Modelling and Simulation in Industrial Applications Applying Energy Optimization to Large Scale Systems DI Matthias Rößler DI Irene Hafner dwh simulation services
Apr 11, 2017
Modelling and Simulation in Industrial Applications
Applying Energy Optimization to Large Scale Systems
DI Matthias RößlerDI Irene Hafner
dwh simulation services
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
3
Energy Optimization - Motivation
• Energy consumption in production industryapprox. 40% of total energy consumptionin 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
4
Application Projects
Interdisziplinäre Forschung zur Energieoptimierung in Fertigungsbetrieben
(interdisciplinary research for energy optimization in production facilities)
Balanced Manufactoring
5
Interdisziplinäre Forschung zur Energieoptimierung in Fertigungsbetrieben
(interdisciplinary research for energy optimization in production facilities)
6
INFO - Approach
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to achieve economic and ecologic goals
Optimization
Fields of OptimizationEnergy System
Production SystemMachineProcess Building
7
INFO Partial Model: Machines
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to achieve economic and ecologic goals
Optimization
Fields of OptimizationEnergy System
Production SystemMachineProcess Building
8
INFOPartial 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
Building
Production SystemEnergy System
9
INFOMachine 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
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
INFOMachine Tools
3,000
5,000
7,000
9,000
11,000
13,000
Leistung ohne WerkstückLeistung mit Werkstück
Zeit [s]
El. P
ower
[W]
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 withworkpiece
11
INFOMachine 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
TOP
DOWN
BOTTOM
UP
compressor model
machine tool model
physical background and measurement
oven and laser model
Mon Tue Wed Thu Fri Sat Sun Mon0
20406080
100120140160180200
Maschinenpark Shedhalle Kompressoren
elek
tris
che
Leis
tung
in k
W
compressorsmachines
elec
tric
pow
er in
kW
12
INFOMachine 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
Mon Tue Wed Thu Fri Sat Sun Mon0
20406080
100120140160180200
Messung Modell
elec
tric
pow
er in
kW
• 25 machine tools in the production hall
• comparison measurement/model
measurement model
13
INFO Partial Model: Building
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to achieve economic and ecologic goals
Optimization
Fields of OptimizationEnergy System
Production SystemMachineProcess Building
14
INFO – Building III
Output
daylightdependent control of - artificial light- shading
heat output/cooling capacity
zonetemperature
Building ModelInput
weather data
waste heatpeople/devices
waste heat machines
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 OptimizationEnergy System
Production SystemMachineProcess Building
16
INFO Energy System I
Output
required power•heat•electricity•others
CO2
emissions
Energy System ModelInput
weather data
heat output/cooling capacity
zonetemperatures
waste heatmachinesrecoverable
CO2
17
INFO Energy System II
10.1. 8.4. 1.8. 10.1. 8.4. 1.8. 10.1. 8.4. 1.8.
0
500
1000
1500Location Cairo
low utilization
high utilizationmedium utilization
10.1. 8.4. 1.8. 10.1. 8.4. 1.8. 10.1. 8.4. 1.8.
0
500
1000
1500
Location Vienna
Ener
gy D
eman
d [k
Wh]
low utilizationmedium utilization
high utilization
10.1. 8.4. 1.8. 10.1. 8.4. 1.8. 10.1. 8.4. 1.8.
0
500
1000
1500
2000
2500
Location Moscow
high utilizationmedium utilization
low utilization
Scenario 1:
oil heatingcompression chiller
Scenario 2:
Heat pumpabsorption chiller with heat recovery
Scenario 2 heat (cooling) Scenario 2 electricity (heating) Scenario 1 heat (heating)Scenario 1 electricity (cooling)
18
INFO Overall Simulation
Analysis
Modelling
Integrated Simulation
... aiming energy optimization in production facilities
... to achieve economic and ecologic goals
Optimization
Fields of OptimizationEnergy System
Production SystemMachineProcess Building
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 optimizationFormalization 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
20
INFO: Approach
Theoretical Modelling Technical IntegrationGoal: 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
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 modelenergy 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
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
Co-Simulation Control Framework
Simulator 1
Simulator 2
Simulator n…
23
INFO – Co-SimulationLoose Coupling (Jacobi Type)
System 1
System 2
Jacobi Type:
Model Problem:
Extrapolation of y1 and y2
System 1:System 2:
24
INFO – Co-Simulation Loose Coupling (Gauß-Seidl Type)
Gauß-Seidl Type:
System 1
System 2Extrapolation of y2Interpolation of y1
Model Problem: System 1:System 2:
25
INFO – Co-SimulationConsistency
• 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
26
INFO – Co-SimulationBCVTB 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
27
INFO – Co-SimulationBCVTB 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
Co-Simulation Framework (BCVTB)
Machines(MATLAB/Excel)
Energy System (Dymola)
Bui lding(EnergyPlus)
28
INFO – Co-Simulation
Simulation control frameworkBCVTB
Machine SimulationMATLAB/Excel
Building SimulationEnergyPlus
Energy System SimulationDymola
Post - ProcessingMATLAB
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
30
Balanced Manufactoring
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
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“
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
logisticsenergy, material and
information flow
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 balanceEnergy balanceTime balanceCost balance
production machine
production machine
air compressor
waste disposal
production process
Cubeproduction
machine
Cubeproduction
machine
Cubeair
compressor
Cubewaste disposal
Mass balanceEnergy balanceTime balanceCost balance
production process
information and resource flow
35
BaMaCubes: 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
mon
itorin
g da
ta
cont
rol a
ction
ener
gy fl
ow
ener
gy fl
ow
work piece, baking goods, etc.
discretized footprint (costs, CO2)
material flow material flowparameters: 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
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
cont
rol
status
User Interface
BaMa - Virtual Cubes
real production facility
machine
building services building
logisticsenergy, material
and information flow
virtual system
Virtual cube machine
Virtual cube building services
Virtual cube building
Virtual cube logistics
information flow
37
BaMa – Tool Chain
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
BaMa – DEV&DESS I
39
products
“flows”
products & “flows”
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
41
BaMa – DEV&DESS III
Cube
guarantees consistency in the cube description technical feasibility requirements for sustainable implementation scientific acceptance
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
43
Real Cube
BaMa – Cube Workflow II
44
standby
heating
wait
hold
Model(verbal, conceptual, physical, mathematical)
BaMa – Cube Workflow III
45
Formal Cube Description
BaMa – Cube Workflow IV
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, haltenHeizzustand (h): on, off
Masse der Entität im Ofen (m)Wärmekap. der Entität im Ofen (cp)Temperatur im Ofen (T)
BaMa – Cube Workflow V
47
DEV&DESS Formulation of the Cube
Name Kürzel Einheit Datentyp WertebereichEntität E Entität
Attribut: Masse E.m kg Skalar > 0Attribut: Temperatur E.T K Skalar > 0Attribut: Wärmekap. E.cp J/(kg*K) Skalar > 0
Name Kürzel Einheit Datentyp WertebereichEntität E Entität
Attribut: Masse E.m kg Skalar > 0Attribut: Temperatur E.T K Skalar > 0Attribut: Wärmekap. E.cp J/(kg*K) Skalar > 0
Abfall EA EntitätAttribut: Masse EA.m kg Skalar > 0Attribut: Temperatur EA.T K Skalar > 0Attribut: Wärmekap. EA.cp J/(kg*K) Skalar > 0
MaterialflüsseEingänge:
Ausgänge:
BaMa – Cube Workflow VI
48
DEV&DESS Formulation of the Cube
Ausgang • wird nur bei Beendigung des Betriebszustands "halten" ausgegeben• Unterscheidung: Entstehung von Abfall
BaMaCube Workflow VII
49
DEV&DESS-Implementation of the Cube= virtual Cube
BaMaCube Workflow VIII
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 variablesoptimization
target functionparameters feedback
modified parameters
Simulation
BaMa Tool
51
BaMa - OptimizationTarget 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)
52
production
BAMA
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
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
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
55
Conclusion
• energy efficiency: increasing need for simulation based solutions
• two different approaches– co-simulation
(quasi) arbitrary amount of participating simulatorsmost suitable software for every partial systemindividual solvers/time steps for partial systemsloss of accuracy
– DEV&DESS formalismmonolithic approach (one simulator)no accuracy lossneed to formalize (adapt model description)
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