Transcript Workshop Green Ship Technology Conference 2013 Hamburg, 14 March 2013 A Network of EE related EU projects • • • • • • TARGETS STREAMLINE GRIP REFRESH RETROFIT cargoXpress • INNOMAN²SHIP • TEFLES • ULYSSES.

Workshop
Green Ship Technology Conference 2013
Hamburg, 14 March 2013
A Network of EE related EU projects
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•
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TARGETS
STREAMLINE
GRIP
REFRESH
RETROFIT
cargoXpress
• INNOMAN²SHIP
• TEFLES
• ULYSSES
Operation
• Energy Audits
• Dynamic Energy
Modelling
• Surface Roughness Modelling and
Effects
• Onboard Decision-Support System
Energy Audit Scope
•
•
•
To obtain real-time
performance data in order to
benchmark the dynamic
energy modeling tool.
To assess the energy efficiency
of the vessel’s operational
pattern and of onboard energy
consumers.
To assess the energy efficiency
and conservation level
resulting from crew practices.
•To establish energy consumption KPIs.
•To identify equipment, processes, procedures that have real and practical
Energy Saving Potentials (ESPs).
•To assess ESP technical and economical feasibility through Cost-Benefit Analysis
(CBA).
•To rank ESPs and provide a decision-support tool in order to improve shipboard
energy efficiency.
Capesize Bulk Carrier
Summary of Identified ESPs & Ranking
Estimated Benefit ($/year)
Estimated Required Capital Investment ($)
90000
80000
Payback period:
3 years
70000
60000
50000
Payback period:
<1 year
40000
30000
20000
10000
oc
cu
p
la
m
ps
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in
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ca
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iz
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om
og
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ai
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op
M
/E
M
/E
ov
er
ha
ul
0
Panamax Bulk Carrier
Summary of Identified ESPs & Ranking
Estimated Benefit ($/year)
Estimated Required Capital Investment ($)
120000,00
100000,00
Payback period:
3 years
80000,00
60000,00
Zero Cost
40000,00
Payback period:
<1 year
20000,00
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ov
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70.000
In
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VLCC
Summary of Identified ESPs & Ranking
Estimated Benefit ($/year)
40.000
Estimated Required Capital Investment ($)
80.000
Payback period:
3 years
60.000
Zero Cost
Payback period:
<1 year
30.000
20.000
10.000
0
/G
ov
er
ha
ul
m
ai
nt
en
D
an
/G
ce
op
ti m
H
is
ig
ed
h
us
Ef
fic
e
M
ie
in
nc
im
y
M
iz
ot
e
or
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s
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fa
n
op
er
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at
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n
s
in
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ne
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m
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ca
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nd
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nt
la
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ps
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/E
Suezmax Tanker
Summary of Identified ESPs
Estimated Benefit ($/year)
80000
70000
50000
40000
Payback period:
<1 year
30000
Estimated Required Capital Investment ($)
100000
90000
Payback period:
3 years
60000
Zero Cost
Payback period:
<1 year
20000
10000
0
Aframax Tanker
Summary of Identified ESPs
Estimated Required Capital Investment ($)
Estimated Benefit ($/year)
20000
18000
Payback period:
3 years
16000
Payback period:
<1 year
14000
12000
Zero Cost
10000
8000
6000
4000
2000
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w
oc
cu
p
la
nc
e
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Vo
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g
e
la
m
ps
t
sc
en
In
ca
nd
e
l ig
ht
in
g
an
cy
sp
a
tio
n
’s
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s
lig
ht
in
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C
of
cc
om
m
od
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sh
fr
e
C
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VA
H
VA
tio
tu
rn
re
/
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ne
In
M
in
im
iz
at
io
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ai
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at
ra
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fic
ie
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ig
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D
/G
m
ai
nt
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Conclusions/Comparison
Bulk Carriers
Summary of Most Significant ESPs
Estimated Benefit ($/year)
30.000
25.000
20.000
15.000
10.000
5.000
0
M/E
Maintenance
D/G
Maintenance
HVAC System
Operation
CAPESIZE
Minimize E/R
fan operation
PANAMAX
Installation of Replacement of
High Efficiency incandescent
lamps
Motors
HANDYMAX
Total benefit from the above ESPs : Capesize →
Panamax →
Handymax →
50,262 $/year
43,656 $/year
57,747 $/year
Capesize →
Panamax →
Handymax →
33,006 $/year
11,444 $/year
15,592 $/year
Total benefit from zero cost ESPs :
Conclusions/Comparison
Tankers
Summary of Most Significant ESPs
35.000
Estimated Benefit ($/year)
30.000
25.000
20.000
15.000
10.000
5.000
0
M/E
Maintenance
D/G
Maintenance
HVAC System
Operation
VLCC
Minimize E/R
fan operation
SUEZMAX
Installation of Replacement of
High Efficiency incandescent
Motors
lamps
AFRAMAX
Total benefit from the above ESPs : VLCC →
Suezmax →
Aframax →
63,309 $/year
61,270 $/year
35,499 $/year
VLCC →
Suezmax →
Aframax →
51,538 $/year
53,803 $/year
29,494 $/year
Total benefit from zero cost ESPs :
Example for Aframax Tanker:
Dynamic Energy Modeling
Input for Benchmarking
D/G Operational Pattern
M/E Performance
SFOC (ISO Corrected) versus M/E Shaft Power (PS)
Shaft Power-SFOC (Shop Trial)
Shaft Power-SFOC (Sea Trial)
Poly. (Shaft Power-SFOC (Shop Trial))
Poly. (Shaft Power-SFOC (Sea Trial))
Shaft Power - SFOC (Energy Audit)
165
160
155
150
145
SFOC (gr/PSh)
140
135
130
125
120
115
110
105
100
5000
7000
9000
11000
13000
15000
Shaft Power (PS)
D/G Performance
17000
19000
21000
23000
25000
Workshop
Contribution:
Operation / Dynamic Energy
Modelling
Green Ship Technology Conference 2013
Hamburg, 14 March 2013
Motivation & Inspiration
EEDI, EEOI, SEEMP
Energy
efficiency
Environmental
performance
Dynamic Energy Modelling
The way forward
• Modelling the dynamics of energy flows within
complex engineering systems as function of
time
• Accurate assessment of life-cycle fuel costs and
carbon “footprint” early in the design stage and
during operation
• Design for energy efficiency and minimum
environmental impact, alongside other design
objectives
Dynamic Energy Modelling
Integration of energy systems
and components
Components
i
Systems
Sum
Global System/Ship
Sum
Energy domains & interactions
Chemical
energy
FW
System
FO
System
Thermal
energy
Exhaust gas
System
Air
System
Environment
Occupants
LO
System
SW
System
Electric power
System
HVAC
system
Energy domains & interactions
Electrical
energy
Mechanical
energy
Interaction with
Electric Motor
Interaction with Diesel
Engine component
Interaction with
other energy
components
Interaction
with other
energy
systems
Interaction with
Environment
Interaction
with other
energy
systems
Interaction with
Propeller
(Effective thrust)
Interaction
with other
energy
components
Interaction with
Environment
(Resistance)
Dynamic Energy Modelling
Global energy model
Superstructure Component
Auxiliary Energy
Added Resistance
Electric Power System
Wave Resistance
Engine Room Systems
Propeller
Prime Mover Component
Advanced Surface technology
Frictional resistance/
Hull coatings
Time-domain
simulation of
power demand
Optimisation
Capesize bulk carrier
Scenario
• Loading condition (ballast)
• Environmental conditions
Weather dependent resistance after 1
day at sea (200 KN) Sea water
temperature – Constant
• Itinerary
1 day at port (unloading)
2days at sea (ordered speed – 14 knots)
1 day at port (loading)
• Operational input (from crew)
Various valves for heating
One D/G at sea , two D/Gs at port
On/off of various machinery
Case study
Energy systems integrated in the model
• Propulsion System
• Fuel oil Transfer/Purifying/Service System
• Steam/Drain/Feed System
• Sea Water System
• Fresh Water System
Energy Components
Case study
Energy systems
Propulsion System
•
Main Engine, Air Cooler,
Turbo Charger
•
Stern tube
•
Shaft
•
Relative rotative
(numerical)
•
Propeller
•
Hull efficiency
•
Ship
•
Resistance (fictitious)
FO System
•
5 pumps (transfer,
purifier, supply,
circulation)
•
3 tanks (bunker,
settling, service)
•
3 HXs (purifier, M/E,
D/G)
•
1 purifier (FO)
•
Various heating
elements (heating
coils)
•
7 control elements
(4 automatic, 3
manual)
SDF
•
•
•
•
•
•
System
Auxiliary Boiler
Feed Pump
Observation Tank
2 HX (condensate,
FO)
13 HX (various
services)
2 Control
Elements
SW System
FW System
•
3 Pumps (Main, •
6 Pumps (Jacket,
Port,
3 D/Gs, 2
Refrigeration)
Compressors)
•
16 Coolers
•
10 HXs (Jacket,
(various
FW Generator,
services)
D/G cooler, etc.)
•
7 Control
•
10 control
Elements
elements (5
(manual)
automatic, 5
manual)
Case study output
Benchmarking with energy audits
Propulsion System
FO System
SDF System
SW System
FW System
DEM deployment in operation
Life-cycle energy management
•
•
•
•
•
Logistics (at ship and fleet levels)
Schedule bunkering
Account for rough weather
Energy-saving practice
Assessment of energy-saving
guidelines
• Crew training
Workshop
contribution:
Operation / roughness
Green Ship Technology Conference 2013
Hamburg, 14 March 2013
Surface Roughness
•
•
•
•
Recent Experiments (Univ. Newcastle)
indicate the large variation of frictional
resistance (cF ) depending on the surface
condition, an increase of more than 50% is
achieved for calcareous foulings.
In addition to the experiments, new CFD
models have been developed to simulate
the effect of surface roughness. These have
been implemented in RANS codes (FreSCo+)
which are now capable to predict the effect
of additional surface roughness on the
resistance.
There are interesting effects of roughness
parameters on the boundary layer
development along the hull and the
pressure recovery.
Comparison between experimental
(extrapolation) and CFD data show good
agreement.
ks (m)
ks+
ΔCF
ΔCF(%)
1. Best FR
0.000016
3.2
0
0.00%
2. Best SPC
0.000019
3.8
1.63547E-05
1.16%
3. Best CDP
0.000018
3.57
1.24295E-05
0.89%
4. Typical antifouling Schultz (2004)
0.00003
6
6.38677E-05
4.41%
4. Light slime / Deteriorated coating
0.0001
21.7
0.000319692
18.77%
5. Heavy slime
0.0003
70.4
0.000623591
31.05%
0.001
258
0.001095931
44.09%
0.003
845
0.001618063
53.30%
0.01
3000
0.002095555
60.17%
6. Small calcareous fouling
Boundary layer development
7. Medium calcareous fouling
Smooth / rough
8. Heavy calcareous fouling
Hull pressure: Smooth / rough
Experiments-Extrapolation
ks
Hydr.Smooth
ks+
0
CF
DCF
DCF (%)
0.001366698
CFD
CF
DCF
DCF (%)
0.001367
Case 1
0.000025
4.925
0.001406901
0.000040
2.86%
0.001527
0.0001603
10.50%
Case 2
0.00005
10.15
0.001506741
0.000140042
9.29%
0.001619
0.0002523
15.58%
Case 3
0.0001
21.4
0.001684371
0.000317672
18.86%
0.001738
0.0003713
21.36%
Case 4
0.00015
33.2
0.001810525
0.000443826
24.51%
0.001819
0.0004523
24.87%
Case 5
0.0002
45
0.001889941
0.000523242
27.69%
0.001883
0.0005163
27.42%
Case 6
0.00025
57
0.001944985
0.000578287
29.73%
0.001935
0.0005683
29.37%
25
Project title:
Project No.:
Call identifier:
Funding scheme:
RETROFITing ships with new technologies for improved overall environmental footprint
285420
FP7-SST-2011-RTD-1
Collaborative project
GreenSEENet Workshop
Retrofitting Decision Support for
Emission Control and Energy Optimization
Boudewijn Hoogvelt - CMTI – The Netherlands
March 14th, 2013
Hamburg, Germany
Decision Support Systems






March 14th, 2013
General
Relation with RETROFIT project
Retrofitting Systems
Challenges
Architecture
Conclusions
27
Decision Support
Lashing support
 Various existing Decision Support
Systems:
Maintenance support
Fire fighting support
Navigation support
March 14th, 2013
28
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
WP1
Ships in operation
WP2
The RETROFIT ship
WP3
Green technologies
T3.4 Preliminary concept
decision support and
monitoring systems
T3.5 Final concept
decision support and
monitoring systems
WP4
Retrofitting process
March 14th, 2013
29
Retrofitting Decision Support Systems
 DSS are installed on a new-build ships
 Retrofitting needs extras as:
 Installing sensors (torque, emission, ….);
 Implement network for data acquisition
& distribution;
 Interfaces to:
 Propeller pitch, RPM & Torque, Emission
sensors;
 Navigation sensors;
 Loading computer;
 Weather forecast system;
 Satellite connection;
 -----
March 14th, 2013
30
Challenges
The challenges for retrofitting:
 Limited space for installation equipment;
 New cabling is very expensive time consuming;
 No standardisation of interfaces to sensors and
systems;
 Limited bandwidth of existing satellite
connection;
 Potential conflict with international rules and
regulations for connecting to existing systems.
March 14th, 2013
31
Decision Support System Architecture
March 14th, 2013
32
Conclusions
 Decision Support is a ‘green technology’;
 Navigation strategy based on state information;
 Captain is informed on the spot about the consequences of his
strategy;
 Implementation of Decision Support on an existing ship is nonobvious, especially when multiple systems are to be combined;
 Possible with extra attention to sensors, satellite connection,
wireless, ….;
 Ship owner may use the data for decisions on a higher lever and
may unlock data for more in-depth studies.
March 14th, 2013
33
THANK YOU FOR YOUR ATTENTION!
Boudewijn Hoogvelt
Centre for Maritime Technology and Innovation (CMTI),
The Netherlands
Project Coordinator:
Boudewijn Hoogvelt, CMTI, The Netherlands
Phone: +31 6 5068 1908
Email: [email protected]
Website:
www.retrofit-project.eu
March 14th, 2013
34
Decision Support System
Energy audits on 6
vessels (WP1)
DEM software (WP2)
Optimization (WP3)
Monitoring (WP4)
Decision support
tool (WP5)
WP5 tasks:
1.Specification (Mar-Jun 2013)
2.Functionality and GUI (Jul 2013 – Apr 2014)
3.Software integration (May-Sep 2014)
• Development of the hosting software
• Models integration to hosting software
4.Testing and evaluation (Jun-Aug 2014)
5.System Guidelines (Oct-Nov 2014)
Input from previous WPs
Parametric dynamic
energy models
Computer model of
the final configuration
following the
retrofitting phase
Operational
optimization routine
Energy efficiency
database
Bayesian Network
Task 5.1: Specification of onboard
system
Task 5.2: Development of functionality
and GUI of the onboard system
Task 5.3: Software integration
Task 5.4: System testing and evaluation
Task 5.5: Guidelines for installing and
using the system
March
April
May
June
July
August
September
October
November
December
January
February
Year 2
March
April
May
June
July
August
September
October
November
December
January
2015
February
Year 3
2012/
2013 Year 1
WP5: Decision support tool (RTD)
2013
2014
Onboard DSS
A prototype system will be developed and evaluated, and
guidelines for its use and installation will be provided in the
course of REFRESH
Scope & Objectives
Assist the crew to make decisions for optimal energy
management onboard the ship (both in real time and during
voyage planning)
• Transmit information ashore for fleet management and
logistics in the long run
Facilitate life-cycle energy management
Methodology
DEM
Generate database
Define scenarios Operators / Energy
audits / historical
records
Data mining
Validation
Bayesian Networks
DSS
Methodology
Data mining: The process of
discovering meaningful
correlations, patterns, and
trends by sifting through
stored data, using pattern
recognition technologies,
and statistical and
mathematical techniques
Methodology
Bayesian networks
(BN): cause-and-effect
relationships in a set
of diverse variables,
inherently reflecting
the uncertainty of a
given operational
scenario.
Loading
condition
Sea
state
Required
speed (input)
Hull
roughness
Fuel
consumption
The network reflects the information contained in the database
• Nodes  dominant database fields
• Node states  data (statistically processed)
• Connecting arcs  data (statistically processed)
Methodology – Why BN?
By setting values to a number of nodes (i.e. outlining part of an operational
scenario) the output of the BN can be obtained.
Hs = 3.5 m
Ballast
condition
Loading
condition
Sampl
e
inputs
Sea
state
Required
speed (input)
Hull
roughness
Fuel
consumption
157 tonnes/day
Data input
• Manual input by the
crew for selected
scenarios
• Automated input by
connections to
selected machinery
equipment
Output
Implementation of the onboard system will
facilitate
• Comparison with required conditions (precalculated optimal controls or criteria) and advice
for improvement
• On-demand execution of a simulation (direct
implementation of the global ship energy model
for a given scenario)
• Over the life-cycle of the ship the database will
continue to be replenished with real data
Output
Performance monitoring of
various energy systems
simultaneously
Comparison of
predicted and
recorded
performance
over time
Presentation of results in a
band for uncertainty
quantification
Advice for remedial action if
selected thresholds are
exceeded