Estimating shipping s operational efficiency
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1 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
2 UCL_ship_efficiency_forICCT_2013.pdf
3 Global shipping emissions According to IMO 2 nd GHG What might be happening now Annual CO2 emissions What might be likely? Return of BAU EEDI/SEEMP
4 What to measure? Fuel consumption X Cf Payload x distance Fuel consumption X Cf dwt x F x distance = Operational Eff. Steamed? Great circle? = Normalised Operational Eff. sector average
5 Deriving fleet technical and operational characteristics
6 Overview of method Input Output Voyage/route maps S-AIS database Clarksons World Fleet Register Calculation Sorted into ship types Missing data algorithms Extract voyage/ operational detail Resistance and propulsion model Individual ship s operation statistics Energy efficiency calculations Fuel consumption calculations Aggregate statistics Individual ship statistics Literature
7 Play movie all VLCC Thanks to Martin Austwick
8 Play movie aframax voyages
9 Validation of Fuel Consumption Calculation Loaded: Ballast: Fuel consumption, metric tonnes/hr Fuel consumption, metric tonnes/hr Blue = Estimated Green = Measured Ship speed, knots Ship speed, knots
10 Findings
11 VLCC 2-3gCO2/tenm
12 !
13
14 Operational efficiency = CO2 emitted p.a. / transport work done gco2/tenm VLCC
15 Findings: dwt (tonnes) overall efficiency gco2/tnm Crude oil tankers >= < IMO 2 nd GHG (2007) calculated OE, filtered (2011) calculated NOE (2011)
16 Ra3o*of*average*opera3ng*speed*to*design*speed* 1.1% 0.9% gco2/ceunm* 0.7% 0.5% 0.3% 0.1% 200" Normalised*opera8onal*efficiency* Rest%of%fleet% Wallenius%Lines%AB% gco2/ceunm* 180" 0% 5000% 10000% 15000% 20000% 25000% 30000% 35000% 40000% 45000% 50000%!0.1% 160" Dwt*tonnes* 140" 120" 100" 80" 60" 40" 20" Rest"of"fleet" Wallenius"Lines"AB" 0" 0" 5000" 10000" 15000" 20000" 25000" 30000" 35000" 40000" 45000" 50000" Dwt*tonnes*
17 Thank you to INTERTANKO, and to LCS members, particularly the management board:
18 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?
19 Extra details
20
21 Basis for estimating FC op Power out (P me x %MCR) Power out (P ae x %MCR) PC Power Required (speed, payload) 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
22 Bottom up estimates - Information required: What is the annual transport work done (tnm)? What is the annual fuel consumption (t/pa)? Operational - Time in ballast/ loaded - What speed(s)? - How much payload is carried? Technical - Fuel consumption in design condition - Off design (draught, speed) effects - Weather (wind, waves, currents) - Hull fouling and engine wear - Auxiliary load
23 Design condition assumption P me MCR% x P me V d V max Assumes values quoted in IMO 2 nd GHG for design MCR% Could use TPD, but no transparency
24 Estimating annual carbon emissions per ship Total across all operating states i Main (propulsion) Aux Time spent i C = (P me_i.sfc me_i.c f + P ae_i.sfc ae_i.c f ).D i.24 Power output of main engine Specific fuel consumption Fuel carbon factor
25 AIS Reported data Lat/lon Speed over ground Heading Course Port proximity Elapsed time between messages Infrequent Message data ETA Destination Draught Classify vessel state using static machine learning model (trained on vessel fixture data) In port/first message out of port Loitering In transit Align modelled network with reported port calls from AIS 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 Aggregate network to 10 speed and draught states Aggregated operational profile per vessel - Speed/Draught/ Period
26 Deterioration and weather impacts 9% Hull and propeller fouling increase resistance Machinery wear can increase SFOC Coating, sea area, maintenance specific, all unknown Simplistic approach based on empirical data
27 Estimating fuel consumption in the design condition This Study Vessel details: IMO number, Built year, owner, flag Hull characteristics: L,B,T,Dwt,GT,TEU Engine characteristics: Installed power, make/model, SFOC, TPD
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