Analysing the Data

Report
Analysing the Data
Peter Mantel, Managing Director
05th November 2014
BMT Group - History
BSRA
NMI Ltd
British Ship
Research
Association (1887)
National
Maritime
Institute (1909)
British Maritime Technology Ltd
established 1985 (now BMT Group Ltd)
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BMT Group
£156 million
turnover
1,400 consultants
Research led
© BMT SMART Ltd. 2014
Global clients
BMT Smart
Briefingthe|Data
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Analysing
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Introduction
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BMT SMART is the specialist vessel
performance division of the BMT Group, the
leading name in global marine consultancy.
A pioneering provider of fleet and vessel
performance management systems.
Offering a comprehensive suite of products,
consultancy solutions and support services.
We have the ability to help ship owners and
operators manage and optimise vessel
performance, and validate and benchmark
results.
BMT SMART is dedicated to delivering solutions
for better, safer, faster and more efficient fleet
performance management.
© BMT SMART Ltd. 2014
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The role of performance monitoring
On-board
• The design and operation of a vessel and its
systems have a major impact on efficiencies.
•
Managing interactions between design and
operation is vital.
Environmental
• Conditions effecting vessel performance are
dynamic and unpredictable.
•
Ship performance is dependent on many factors
from the quality and type of hull coating to prevailing
weather and oceanographic conditions.
Industry
• External influences on the shipping industry can
significantly effect overall vessel, voyage and fleet
operation.
•
Having the right data at hand enables better
management of the response to specific challenges.
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How to measure vessel performance
1
Vessel performance data is automatically
collected on-board as your advanced BMT
SMART solution interfaces with systems
and sensors.
2
Panel displays and on-board computers
can be used to present key trending
information and live feedback continually to
the crew.
3
Satellite communications are used to
automatically relay the vessel’s
performance data ashore while also
updating the on-board system.
4
All of this information is stored securely on
our servers, where it is modelled with our
high-quality Metocean data.
5
Our web-based platform provides easy and
intuitive access to manage and analyse
vessel and fleet performance.
3
2
1
4
5
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How to understand vessel performance
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On its own, data will not improve vessel
performance
You need a solution that provides sophisticated
data collection, display and analysis services to
support optimal decision-making
This is achieved through a simple four-step
approach:
1. Measure
2. Manage
3. Analyse
4. Action
In this presentation we discuss the analysis step
further.
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Analysis techniques
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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
Filtering
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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.
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What is normalisation?
Model test
baseline
25000
20000
In service data
Power (MW)
15000
10000
5000
0
0
5
10
15
20
Speed (kts)
Model Tests Base Condition
Input Dataset
Before Normalising
After Normalising
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What is normalisation?
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Normalisation relies solely on a full
and accurate model to avoid any
uncertainty introduced to the analysis.
25000
20000
•
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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.
There is the risk of under or over
correction to the dataset.
This can lead to misinterpretation of
the performance data.
15000
Power (MW)
•
10000
5000
0
0
5
10
15
Speed (kts)
Model Tests Base Condition
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Input Dataset
Over
corrected
20
data
Corrected Dataset
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Why filtering?
Filtered for:
• Draught
• Wind Speed
• Wave Height
• Current Speed
Before Filtering
•
After Filtering
Filtering requires a large dataset to ensure that after filtering there is still sufficient data to produce reliable
analysis.
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What do we do?
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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
• 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.
•
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.
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BMT’s approach to analysis - Coefficients
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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
© BMT SMART Ltd. 2014
The SFOC, Propeller and Hull
Coefficients:
Isolate the individual
components giving the efficiency
of each independently
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The importance of sensor accuracy
Studies show that the precision of the speed sensor is fundamental to reducing uncertainty in analysis.
Performance Indicator Bias
2000
MC Simulation: Average Performance Indicator,
kW
•
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
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The importance of accurate speed through water
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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.
This can be done by comparing derived Speed through Water (by correcting Speed Over Ground for
ocean and tidal currents) with Log Speed readings.
25
20
20
SOG - tides - ocean (kn)
25
15
SOG (kn)
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10
5
0
-5
10
5
0
0
-5
15
5
10
15
20
25
-5
0
-5
Log speed (kn)
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5
10
15
20
25
Log speed (kn)
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Comparing data collection techniques
Changes to Input Uncertainties
Effect of Input Uncertainties on CM and NR baselines for Different
Evaluation Periods
4
20
Model error
27
5
146
24
Full days
44
12
211
62
Baseline
50
0
50
255
100
150
200
250
300
Uncertainty as a Percentage of the change in Ship Performance
CM 9 months
CM 3 months
NR 9 months
NR 3 months
Simulation uncertainty sensitivity analysis results, L. Aldous et al. (2014)
•
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.
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Changes to Input Uncertainti
20
Model error
27
Comparing data collection techniques
5
146
24
Full days
44
12
211
62
Baseline
50
0
50
255
100
150
200
250
300
Uncertainty as a Percentage of the change in Ship Performance
CM 9 months
•
NR 9 months
NR 3 months
3 months of continuous monitoring data shows a very similar level of uncertainty, when calculating
baselines, as 9 months of noon reporting data.
3 months
Continuous Monitoring
•
CM 3 months
≈
9 months
Noon Reporting
This shows that there is a significant benefit in continuous monitoring for reducing uncertainty in data.
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What to measure?
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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.
Shaft Power vs Log Speed
Fuel Consumption vs Log Speed
It is often useful to isolate the propulsive
efficiency (that of the hull and propeller) from the
engine.
In this case it is the vessel speed against shaft
power relationship that becomes relevant.
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The Power Coefficient
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The Power Coefficient identifies the combined effects of the efficiencies of the hull and the propeller.
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.
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.
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What data is required?
Vessel Data
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Fuel Consumption (Mass)
Fuel Quality (Calorific Value)
Shaft Power
Metocean Data
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Wind Speed
Wave Height
Current Speed
Speed Through Water
Draught
Trim
Propeller RPM
Propeller Pitch (CPP vessels)
Water Temperature
Water Salinity
Speed Over Ground
Vessel Particulars
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Length
Beam
Design Draught
Block Coefficient
Wetted Surface Area
Service Speed
Engine Brake Power
Propeller Diameter
Number of Propeller Blades
Propeller Blade Area Ratio
Propeller Design Pitch
Heading
Water Depth
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Data acquisition
 Marine
approved
Bridge
15” Touchscreen
Display
Ship’s Network
Ship’s Power
Windows 7
SSD PC
ECDIS
GPS
 Modular
 10 year parts
guarantee
Echo Sounder
Gyro
24v - UPS
Speed Log
Rudder Angle
8 x Serial
Interface
VDR
RS232/422/485
Dual LAN
For Redundancy
IAS/EMS
ECR/CCR
24 x Analogue
Interface
M/E In
Mass Flow
e.g. 0-10V,
4-20mA
1 x Serial
D/G Interface
In Volume Flow
RS232/422/485
D/G Out Volume Flow
Shaft Power
Shaft RPM
Shaft Torque
Draught FWD
Fuel Temperature
Draught AFT
Tank Levels
Propeller Pitch
Trim
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Vessel View
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Analysis examples: Stationary period and hull scrub
Stationary period and hull scrub
• LNG Tanker operating in the Middle East.
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Analysis examples: Effect of New Coating
Stationary period and hull scrub
• LNG Tanker operating in the Middle East.
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Analysis examples: Dry-docking
Dry-docking
• VLCC Tanker before and after dry-dock.
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Carbon Credits : A Marine Industry First
International Paint have worked with The Gold Standard to develop the first approved
methodology to generate carbon credits for the marine industry
• The methodology is both unique and pioneering.
– 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.
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Carbon Credits : A Marine Industry First
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
– Ship operators are financially rewarded for emission reductions
– 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
• The emission-saving of Intersleek® is determined and directly related to the amount of carbon credits
generated
– 1 tonne CO2 saved = 1 carbon credit
All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale.
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The Methodology
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®
CO2 emissions as a function of Speed
• The same data source is then used to determine the emission savings after the application of Intersleek ®
Baseline Data from
first dock cycle
Daily CO2 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.
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Vessel Eligibility
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
• 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
• 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
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Summary
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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.
Assessing the relationships between different recorded
parameters offers the ability to trend vessel performance
over time.
This can be used to monitor efficiencies and quantify the
effectiveness of maintenance interventions; introduction of
ESDs, participating in Carbon Credit Initiative, etc.
Industry needs to embrace this technology
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Thank you.
Any questions?

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