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