th

British Ship
Research
Association (1887)
National
Maritime
Institute (1909)
established 1985 (now BMT Group Ltd)
© BMT SMART Ltd. 2014 Analysing the Data | 2

£156 million turnover
1,400 consultants Research led Global clients
© BMT SMART Ltd. 2014
BMT Smart Briefing | 3
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BMT SMART is the specialist vessel performance division of the BMT Group, the leading name in global marine consultancy.
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A pioneering provider of fleet and vessel performance management systems.
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Offering a comprehensive suite of products, consultancy solutions and support services.
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We have the ability to help ship owners and operators manage and optimise vessel performance, and validate and benchmark results.
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BMT SMART is dedicated to delivering solutions for better, safer, faster and more efficient fleet performance management .
© BMT SMART Ltd. 2014 Analysing the Data | 4

On-board
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The design and operation of a vessel and its systems have a major impact on efficiencies.
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Managing interactions between design and operation is vital.
Environmental
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Conditions effecting vessel performance are dynamic and unpredictable.
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Ship performance is dependent on many factors from the quality and type of hull coating to prevailing weather and oceanographic conditions.
Industry
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External influences on the shipping industry can significantly effect overall vessel, voyage and fleet operation.
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Having the right data at hand enables better management of the response to specific challenges .
© BMT SMART Ltd. 2014 Analysing the Data | 5

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Vessel performance data is automatically collected on-board as your advanced BMT
SMART solution interfaces with systems and sensors.
Panel displays and on-board computers can be used to present key trending information and live feedback continually to the crew.
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Satellite communications are used to automatically relay the vessel’s performance data ashore while also updating the on-board system.
All of this information is stored securely on our servers, where it is modelled with our high-quality Metocean data.
Our web-based platform provides easy and intuitive access to manage and analyse vessel and fleet performance.
© BMT SMART Ltd. 2014 Analysing the Data | 6

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On its own, data will not improve vessel performance
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You need a solution that provides sophisticated data collection, display and analysis services to support optimal decision-making
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This is achieved through a simple four-step approach:
1.
Measure
2.
Manage
3.
Analyse
4.
Action
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In this presentation we discuss the analysis step further.
© BMT SMART Ltd. 2014 Analysing the Data | 7

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There are two accepted methods for preparing vessel performance data for analysis:
Normalising corrects the vessel data for the variance caused by weather etc. through the use of a vessel model that predicts the vessels performance for all operating conditions.
Filtering removes the variance caused by weather, load condition, water depth etc. by filtering the dataset.
Normalisation
Raw
Performance Data
Prepared
Performance Data
Analysis and
Trending
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BMT’s approach is to automate the collection of data to allow for a sufficiently large dataset to filter.
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We believe that the risks of correcting the data through normalisation may lead to misinterpretation.
© BMT SMART Ltd. 2014 Analysing the Data | 8

25000
20000
15000
10000
5000
0
0
In service data
5 10
Model Tests Base Condition
Speed (kts)
15
Input Dataset
Before Normalising
20
Model test baseline
After Normalising
© BMT SMART Ltd. 2014 Analysing the Data | 9

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Normalisation relies solely on a full and accurate model to avoid any uncertainty introduced to the analysis.
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This typically requires a full set of model tests for various load conditions for a full range of wind and waves.
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There is the risk of under or over correction to the dataset.
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This can lead to misinterpretation of the performance data.
25000
20000
15000
10000
5000
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0 5
Model Tests Base Condition
10
Speed (kts)
Input Dataset
15
Corrected Dataset
Over corrected
20 data
© BMT SMART Ltd. 2014 Analysing the Data | 10

Filtered for:
• Draught
• Wind Speed
• Wave Height
• Current Speed
Before Filtering After Filtering
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Filtering requires a large dataset to ensure that after filtering there is still sufficient data to produce reliable analysis.
© BMT SMART Ltd. 2014 Analysing the Data | 11

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L. Aldous et al. (2014) suggests, in her analysis of ship performance monitoring uncertainty, that there is a trade off between the uncertainties introduced by modelling data (normalisation) and the uncertainties from a smaller dataset (filtering).
BMT’s Approach
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With an automated acquisition system and high quality Metocean data, filtering is easy and reliable with a sufficient quantity of data to reduce the uncertainties.
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BMT employ a filtering technique and our own derived performance indicators based on high quality, high quantity in-service data from our automated performance monitoring software.
© BMT SMART Ltd. 2014 Analysing the Data | 12

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Once the data has been filtered coefficients are calculated, giving the powerful tool of trending isolated performance components.
The Fuel Coefficient:
Identifies the fuel flow and the log speed. This gives the overall vessel performance including the engine, propeller and hull
The Power Coefficient:
Identifies the shaft power and log speed. This gives the overall efficiency of the propeller and hull excluding the engine
The SFOC, Propeller and Hull
Coefficients:
Isolate the individual components giving the efficiency of each independently
© BMT SMART Ltd. 2014 Analysing the Data | 13

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Studies show that the precision of the speed sensor is fundamental to reducing uncertainty in analysis .
Performance Indicator Bias
2000
1500
1000
500
0
-500
-1000
-1500
-2000
Performance indicator bias, L. Aldous et al. (2014)
The error bars indicate the precision, the redline is the performance indicator of the baseline
© BMT SMART Ltd. 2014 Analysing the Data | 14

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Using Speed over Ground instead of Speed through Water can have a dramatic effect on uncertainty.
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Speed Log sensor drift has a significant effect on performance indicators.
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This highlights the importance of being able to monitor sensor drift.
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This can be done by comparing derived Speed through Water (by correcting Speed Over Ground for ocean and tidal currents) with Log Speed readings.
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20
15
10
5
-5
0
0
-5
5 10
Log speed (kn)
15 20 25
20
15
10
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5 10
Log speed (kn)
15 20 25
© BMT SMART Ltd. 2014 Analysing the Data | 15

Effect of Input Uncertainties on CM and NR baselines for Different
Evaluation Periods
Model error
4
20
27
146
Full days
Baseline
0
5
24
44
211
12
50
62
50 100 150 200 250
Uncertainty as a Percentage of the change in Ship Performance
CM 9 months CM 3 months NR 9 months NR 3 months
255
300
Simulation uncertainty sensitivity analysis results, L. Aldous et al. (2014)
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A low sample frequency (i.e. noon reporting) requires a much longer time than a high sample frequency method (i.e. continuous (automated) monitoring) to reach the same level of certainty.
© BMT SMART Ltd. 2014 Analysing the Data | 16

Effect of Input Uncertainties on CM and NR baselines for Different
Evaluation Periods
4
20
Model error
27
5
24
Full days
44
211
12
62
Baseline
50
0 50 100 150 200 250
Uncertainty as a Percentage of the change in Ship Performance
CM 9 months CM 3 months NR 9 months NR 3 months
255
300
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3 months of continuous monitoring data shows a very similar level of uncertainty, when calculating baselines, as 9 months of noon reporting data.
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This shows that there is a significant benefit in continuous monitoring for reducing uncertainty in data.
© BMT SMART Ltd. 2014 Analysing the Data | 17

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For a vessel, the most complete measure of efficiency is the relationship between vessel speed and shaft power or fuel consumption .
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It is often useful to isolate the propulsive efficiency (that of the hull and propeller) from the engine.
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In this case it is the vessel speed against shaft power relationship that becomes relevant.
Shaft Power vs Log Speed
Fuel Consumption vs Log Speed
© BMT SMART Ltd. 2014 Analysing the Data | 18

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The Power Coefficient identifies the combined effects of the efficiencies of the hull and the propeller.
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Increased power absorption, due to the effect of fouling on the hull or propeller for example, is directly reflected in an increase of the Power Coefficient.
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A value of 1.10 is interpreted as an increase of 10% in shaft power to achieve a given vessel speed when compared to baseline.
© BMT SMART Ltd. 2014 Analysing the Data | 19

Vessel Data
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Fuel Consumption (Mass)
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Fuel Quality (Calorific Value)
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Shaft Power
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Speed Through Water
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Draught
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Trim
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Propeller RPM
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Propeller Pitch (CPP vessels)
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Water Temperature
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Water Salinity
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Speed Over Ground
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Heading
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Water Depth
Metocean Data
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Wind Speed
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Wave Height
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Current Speed
Vessel Particulars
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Length
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Beam
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Design Draught
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Block Coefficient
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Wetted Surface Area
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Service Speed
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Engine Brake Power
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Propeller Diameter
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Number of Propeller Blades
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Propeller Blade Area Ratio
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Propeller Design Pitch
© BMT SMART Ltd. 2014 Analysing the Data | 20

Bridge
15” Touchscreen
Display
Ship’s Network
ECDIS
GPS
Echo Sounder
Gyro
Speed Log
Rudder Angle
8 x Serial Interface
VDR
RS232/422/485
IAS/EMS
ECR/CCR
Shaft Power
Shaft RPM
Shaft Torque
Draught FWD
Draught AFT
Propeller Pitch
Dual LAN
For Redundancy
© BMT SMART Ltd. 2014
Ship’s Power
Windows 7
SSD PC
24v - UPS
 Marine approved
 Modular
 10 year parts guarantee
24 x Analogue
Interface
RS232/422/485
D/G Out Volume Flow
Fuel Temperature
Tank Levels
Trim
Analysing the Data | 21

© BMT SMART Ltd. 2014 Analysing the Data | 22

Stationary period and hull scrub
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LNG Tanker operating in the Middle East.
© BMT SMART Ltd. 2014 Analysing the Data | 23

Stationary period and hull scrub
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LNG Tanker operating in the Middle East.
© BMT SMART Ltd. 2014 Analysing the Data | 24

Dry-docking
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VLCC Tanker before and after dry-dock .
© BMT SMART Ltd. 2014 Analysing the Data | 25

International Paint have worked with The Gold Standard to develop the first approved methodology to generate carbon credits for the marine industry
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The methodology is both unique and pioneering.
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First for the marine industry
– First to consider moving articles (ships)
– First to go beyond geographic boundaries
All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale.
© BMT SMART Ltd. 2014 Analysing the Data | 26

Generation of carbon credits means that emission savings from green technologies are independently verified
– Reductions in greenhouse gas emissions and therefore fuel savings from retrofitting efficiency improving technologies can be independently verified
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Ship operators are financially rewarded for emission reductions
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New source of finance in a difficult market
To qualify for carbon credits ship owners are required to upgrade their vessels from a biocide-containing traditional antifouling to Intersleek
® technology
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The emission-saving of Intersleek
® generated is determined and directly related to the amount of carbon credits
–
1 tonne CO
2 saved = 1 carbon credit
All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale.
© BMT SMART Ltd. 2014 Analysing the Data | 27

The methodology is based on data received from ships which is then translated into greenhouse gas emission savings
• A baseline emission level is determined prior to the application of Intersleek
®
• The same data source is then used to determine the emission savings after the application of Intersleek
®
Baseline Data from first dock cycle
Daily CO
2
Saving
Data from first year of
Intersleek
® application
Time (days since dock)
All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale .
© BMT SMART Ltd. 2014 Analysing the Data | 28

A vessel becomes eligible when it is converted from a biocidal antifouling to
Intersleek
®
• Carbon credits can be claimed until the vessel has been recoated
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Vessels with intermediate dockings (2 or 3 year dock cycles) can continue to claim for each dock cycle until they are recoated
Vessels become ineligible for claiming credits if
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The Intersleek
® is recoated
• Any other energy-saving device is installed (e.g. PCBF, Mewis Duct)
All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale.
© BMT SMART Ltd. 2014 Analysing the Data | 29

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There is clearly a trade off between modelling data through normalisation and reducing dataset size through filtering. With a large enough dataset of sufficient quantity, filtering offers a reliable mechanism for preparing data for analysis.
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Assessing the relationships between different recorded parameters offers the ability to trend vessel performance over time.
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This can be used to monitor efficiencies and quantify the effectiveness of maintenance interventions; introduction of
ESDs, participating in Carbon Credit Initiative, etc.
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Industry needs to embrace this technology
© BMT SMART Ltd. 2014 Analysing the Data | 30