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www.napa.fi
Supporting the Operational Efficiency of LNG Carriers with Big Data and IoT(Internet of Things)
Deok-Hoon Jang / DongSup Shin, Feb 7th, 2017
Technical Streams
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Visibility
Intelligence
Control
Inherent Complexity in
Operation & Technology
LNG Shipping Transportation
The Background
Greater Demand forUtilization of Operation Data and
Analysis of Ship Performances
3 Key Elements
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Key Elements of Ship Data Management & Analysisin LNG Carrier Operations
Visibility ControlIntelligence
• Onboard Monitoring• On-shore Monitoring• Generic KPIs• LNG Cargo Tank Status
History• Cool-down Rate• Boil-off Rate• Fleet Performance
Comparison• User Interface &
Visualization
• Dynamic Performance Model
• Vessel Performance Analysis
• Hull & Propeller Performances
• Performance Dashboard• Specific Analysis – Cool-
down Process Model
• Voyage Planning• Vessel Speed & Route
Optimization• Schedule Optimization• Hull & Propeller
Management• Trim Optimization• LNG Tank Management
3
© NAPA
Visibility in LNGC Operation
Visibility ControlIntelligence
• Onboard Monitoring• On-shore Monitoring• Generic KPIs• LNG Cargo Tank Status
History• Cool-down Rate• Boil-off Rate• Fleet Performance
Comparison• User Interface &
Visualization
• Vessel Performance Analysis
• Hull & Propeller Performances
• Dynamic Performance Model
• Performance Dashboard• Specific Analysis – Cool-
down Process Model
• Voyage Planning• Vessel Speed & Route
Optimization• Schedule Optimization• LNG Tank Management• Hull & Propeller
Management
4
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Monitoring on Board
5
Visibility
Key Figures for:
• Fuel Consumptions
• Cargo Tank Temperatures
• Emissions
• Cool Down Rates
• LNG-specific Key Figures for Boil-off Utilization
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Data Collection & Monitoring on Shore
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Data Interface from:• Loading computer• NAPA Trim Optimization• NAPA Real Time Monitoring• NAPA Voyage Optimization• 3rd Party systems…
Server DB• Navigational data• Propulsion-related
data
• Record vessels performance data • Automatically creates voyage reports• Ship performance and voyage data
available at the office• Monitor fleet performance • Benchmark and analyse ships• Trim monitoring
In every 5 mins
Visibility
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Monitoring on Shore – Boil-off Gas and Rate
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Visibility
Time
Boil-o
ff (m
3)
Perc
enta
ge o
f T
ota
l Tank C
apacity (
%)
© NAPA
Intelligence in LNGC Operation
Visibility ControlIntelligence
• Onboard Monitoring• On-shore Monitoring• Generic KPIs• LNG Cargo Tank Status
History• Cool-down Rate• Boil-off Rate• Fleet Performance
Comparison• User Interface &
Visualization
• Dynamic Performance Model
• Vessel Performance Analysis
• Hull & Propeller Performances
• Performance Dashboard• Specific Analysis – Cool-
down Process Model
• Voyage Planning• Vessel Speed & Route
Optimization• Schedule Optimization• LNG Tank Management• Hull & Propeller
Management
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© NAPA
Dynamic Performance Model
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Measured Predicted
Self-learning (Dynamic Performance Model)
Effect Verification (Power Estimation Accuracy)
Time
Po
wer
Wind and waves in the performance is able to accurately estimate
* Weather condition: wind 0-20 m/s, wave 0-2 m
Example
Intelligence
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Performance Analysis – Filtering out External Conditions
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Dynamic Performance Model (DPM)
Automatic analysis based on collected voyage data
Customer can get reliable baseline
Differentiated point from others
Speed [kn]
Po
wer [
kW
]Speed-power curve
All data
Filtered
Normalized
Power curve
Intelligence
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• Model predicts required time or final tank temperature for cooling and warming periods during ballast voyages.
• Possible optimization of
• Energy consumption
• LNG consumption
• Cooling time
• Requires
• LNG usage constraints - Spay amount of LNG
• Time constraints
• Target temperature in the tank
• Estimated time of arrival
Specific Analysis – Cool Down Model usage examples
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Intelligence
© NAPA
Control in LNGC Operation
Visibility ControlIntelligence
• Onboard Monitoring• On-shore Monitoring• Generic KPIs• LNG Cargo Tank Status
History• Cool-down Rate• Boil-off Rate• Fleet Performance
Comparison• User Interface &
Visualization
• Vessel Performance Analysis
• Hull & Propeller Performances
• Dynamic Performance Model
• Performance Dashboard• Specific Analysis – Cool-
down Process Model
• Voyage Planning• Vessel Speed & Route
Optimization• Schedule Optimization• Hull & Propeller
Management• Trim Optimization• LNG Tank Management
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© NAPA
Voyage Optimization for LNG Carrier
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Input Variable
Manual:• Vessel Information• Route Plan• Schedule (ETD, ETA)
Dynamic Performance Model• Navigational data• Propulsion-related data
Auto:• Weather Information
Output Variable
Optimal RouteOptimal Speed ProfileVoyage Information• ETA• Average Speed• Duration• Fuel Consumption• Fuel cost
Control
Better Accuracy
andValues in:
Money Time Safety
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Voyage Optimization for LNG Carrier
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Case Study Example
Route
/Speed
Optim
ization
Schedule
O
ptim
ization
33.5% Savings Potential
Control
~155,000* USD saved in one voyage
* 330 USD/mt
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Hull & Propeller Management
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Control
Annual Average Fuel Consumption
x Average Decrease
(improvement) Rate (%) =
Value of Propeller polishing (or Hull
cleaning)
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Fleet Management
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Control
Normalized speed curves of different ships in a fleet gives
Financial Insight
Example:
Performance Gap (%) x Annual Fuel Consumption of a ship x Fuel cost (USD/mt) =
Savings Potential
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Cargo Tank Temperature & Pressure Optimization
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Control
Case Study Example
Monitor Analysis and Research Better control on how to
manage tank temperature & pressure in terms of Energy consumption Man hour (Crew) Heel amount (Maximize cargo utilization)
© NAPA
Key Elements of Ship Data Management in LNGC Operation
Visibility ControlIntelligence
• Onboard Monitoring• On-shore Monitoring• Generic KPIs• LNG Cargo Tank Status
History• Cool-down Rate• Boil-off Rate• Fleet Performance
Comparison• User Interface &
Visualization
• Dynamic Performance Model
• Vessel Performance Analysis
• Hull & Propeller Performances
• Performance Dashboard• Specific Analysis – Cool-
down Process Model
• Voyage Planning• Vessel Speed & Route
Optimization• Schedule Optimization• Hull & Propeller
Management• Trim Optimization• LNG Tank Management
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