roessler, hafner - modelling and simulation in industrial applications: applying energy optimization...

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)

THANK YOU FOR YOUR ATTENTION!