Estimating shipping’s operational efficiency - Intertanko Efficiency - Tristan Smith.pdf · Estimating shipping’s operational efficiency Tristan Smith, UCL Energy Institute With
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Estimating shipping’s operational efficiency
Tristan Smith, UCL Energy Institute
With gratitude to my colleagues: Eoin O’Keeffe, Lucy Aldous tristan.smith@ucl.ac.uk
http://www.theicct.org/sites/default/files/publications/UCL_ship_efficiency_forICCT_2013.pdf
Global shipping emissions
2010 2050
Annual CO2 emissions
2030
According to IMO 2nd GHG
Return of ‘BAU’
What might be happening now
What might be likely?
EEDI/SEEMP
What to measure?
Fuel consumption X Cf
Payload x distance = Operational Eff.
Fuel consumption X Cf dwt x F x distance
= Normalised Operational Eff.
Steamed? Great circle?
sector average
Deriving fleet technical and operational characteristics
Overview of method
S-AIS database
Clarksons World Fleet Register
Literature
Input
Fuel consumption calculations
Energy efficiency calculations
Individual ship’s operation statistics Sorted
into ship types
Missing data algorithms
Resistance and propulsion model
Extract voyage/operational detail
Calculation
Voyage/route maps
Output
Aggregate statistics
Individual ship statistics
• Play movie – all VLCC
Thanks to Martin Austwick
• Play movie – aframax voyages
Validation of Fuel Consumption Calculation
Loaded: Ballast:
0 5 10 15 200
0.5
1
1.5
2
2.5
3
Ship speed, knots
Fuel
con
sum
ptio
n, m
etric
tonn
es/h
r
0 5 10 15 200
1
2
3
4
5
6
Ship speed, knots
Fuel
con
sum
ptio
n, m
etric
tonn
es/h
rBlue = Estimated Green = Measured
Findings
2-3gCO2/tenm
VLCC
!
Operational efficiency = CO2 emitted p.a. / transport work done
1.5-8 gCO2/tenm
VLCC
Findings:
dwt (tonnes) overall efficiency gCO2/tnm
>= <
IMO 2nd GHG (2007)
calculated OE, filtered (2011)
calculated NOE (2011)
Crude oil tankers 80000 120000 10.9 12.8 10.8
120000 200000 8.1 8.5 6.0
200000 + 5.4 6.4 4.3
!0.1%
0.1%
0.3%
0.5%
0.7%
0.9%
1.1%
0% 5000% 10000% 15000% 20000% 25000% 30000% 35000% 40000% 45000% 50000%
gCO2/CE
Unm
*
Dwt*tonnes*
Ra3o*of*average*opera3ng*speed*to*design*speed*
Rest%of%fleet%
Wallenius%Lines%AB%
0"
20"
40"
60"
80"
100"
120"
140"
160"
180"
200"
0" 5000" 10000" 15000" 20000" 25000" 30000" 35000" 40000" 45000" 50000"
gCO2/CE
Unm
*
Dwt*tonnes*
Normalised*opera8onal*efficiency*
Rest"of"fleet"
Wallenius"Lines"AB"
www.lowcarbonshipping.co.uk
Thank you to INTERTANKO, and to LCS members, particularly the management
board:
Questions
- Are we looking at the right variables? - What is the right mix of technical and commercial? - What other analysis using this data would be
interesting? - Can you align what’s useful for your commercial
purposes to the MRV debate? - How are energy efficiency measurements best
shared: - Within an organisation? - With other stakeholders?
Extra details…
Basis for estimating FCop
1. Estimate power required in design specification (Holtrop & Mennen)
2. Estimate power required in given state (speed, payload, fouling/deterioration, weather)
3. Apply delta to installed power and design %MCR 4. Calculate new %MCR and corresponding SFOC 5. Calculate fuel consumption in given state
Power Required (speed, payload)
Power out (Pme x %MCR) Power out (Pae x %MCR)
PC
Bottom up estimates - Information required:
What is the annual fuel consumption (t/pa)?
What is the annual transport work done (tnm)?
- Fuel consumption in ‘design’ condition
- Off design (draught, speed) effects
- Weather (wind, waves, currents)
- Hull fouling and engine wear
- Auxiliary load
Technical
- Time in ballast/loaded
- What speed(s)? - How much
payload is carried?
Operational
‘Design’ condition assumption
• Assumes values quoted in IMO 2nd GHG for design MCR%
• Could use TPD, but no transparency
Vd
Pme
Vmax
MCR% x Pme
Estimating annual carbon emissions per ship
C = (Pme_ i.sfcme_ i.Cf +Pae_ i.sfcae_ i.Cf ).Di.24i∑
Power output of main engine Specific fuel
consumption
Fuel carbon factor
Main (propulsion) Aux Time spent
Total across all operating states ‘i’
AIS Reported data • Lat/lon • Speed over ground • Heading • Course • Port proximity • Elapsed time between messages
Infrequent Message data • ETA • Destination • Draught
• In port/first message out of port • Loitering • In transit
Classify vessel state using static machine learning model (trained on vessel fixture data)
Time stamped O-D matrix for each vessel
Remove anomalous states and resolve port/loitering states to port locations
Normalized Vessel Network • Speed profile on each voyage • Draught condition on a subset
Align modelled network with reported port calls from AIS
Aggregated operational profile per vessel - Speed/Draught/Period
Aggregate network to 10 speed and draught states
Deterioration and weather impacts
• Hull and propeller fouling increase resistance • Machinery wear can increase SFOC • Coating, sea area, maintenance specific, all
unknown • Simplistic approach based on empirical data
9%
Estimating fuel consumption in the ‘design’ condition
• Vessel details: – IMO number, Built year, owner, flag…
• Hull characteristics: – L,B,T,Dwt,GT,TEU…
• Engine characteristics: – Installed power, make/model, SFOC, TPD
This Study
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