Challenges in Vessel Speed Optimization Jussi Pyrre Vice President - - PowerPoint PPT Presentation

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Challenges in Vessel Speed Optimization Jussi Pyrre Vice President - - PowerPoint PPT Presentation

Jan 24, 2024 13 likes •338 views

Challenges in Vessel Speed Optimization Jussi Pyrre Vice President of Technology Eniram in brief Key points Operations by geography Leading provider of intelligent decision support systems and analytics for ships and operators

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Challenges in Vessel Speed Optimization

Jussi Pyörre

Vice President of Technology

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Eniram in brief

Key points Operations by geography Key Facts Current products and services

Eniram Onboard Applications Eniram Performance Managers

  • Management of entire

fleets with a consistent view of improving fuel efficiency and reducing emissions

  • Vessel based system

for dynamically monitoring and

  • ptimizing vessel

performance

Major distinctions

Eniram Analytics

  • Enables customers to

get more detailed insight into areas of

  • perations

Leading provider of intelligent decision support systems and analytics for ships and operator’s

  • Established in 2005
  • Marine experts, software

designers, data scientists, naval architects and captains

  • Enjoying significant growth

 Eniram offers real-time vessel optimization and strategic guidance to the shipping industry

Facilities: Helsinki (HQ), Fort Lauderdale, Boston, London, Singapore Sales & Service Partners: Singapore, Hamburg, Oslo, Miami, Shanghai, Genoa

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DATA DRIVEN

150 vessels 60,000 sea days 700M+ measurements / day 9,000 measurements / second

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Vessel Platform

Sensor information Integration to Bridge System Integration to Automation Attitude

Data Center

Onboard Applications Energy Management

Eniram solutions

Navigation Automation Optimize Follow-up

Dynamic Trimming Assistant Optimum Speed Assistant Vessel Performance Manager Fleet Performance Manager Fouling Analysis

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How to arrive in time – with the least fuel consumed?

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Should you ... find optimal currents? ... optimize engine/propeller load? ... slow down on shallow water? ... and how much?

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63NM 12 % 25 MT HFO

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5 10 15 20 25 5 10 15 20

Power [MWh] Speed [kn]

Speed-Power

155 160 165 170 175 180 50 60 70 80 90 100

SFOC [g/kWh] Load [%]

Engine

0% 10% 20% 30% 40% 50% 60% 70% 80% 0,5 1 1,5

Efficiency Advance Coefficient

Propeller

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CHALLENGES IN SPEED OPTIMIZATION

  • ETA estimation: changing conditions
  • Hull, Propeller & Engine: off-design & degradation
  • Weather forecasts: reliability, accuracy
  • Obtainable service speed: changing conditions
  • Hydrodynamic and aerodynamic modelling: accuracy
  • Data: quality, sufficiency
  • Frequent speed adjustments
  • End-user acceptance: ease-of-use, involvement
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Ingredients of Optimization

Fuel Consumption Model Operating Conditions Constraints Optimization Algorithm Optimal Speed Profile

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Constraints

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Ingredients of Optimization

Fuel Consumption Model Operating Conditions Constraints Optimization Algorithm Optimal Speed Profile

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Operating Conditions

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Operating Conditions

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Operating Conditions

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Ingredients of Optimization

Fuel Consumption Model Operating Conditions Constraints Optimization Algorithm Optimal Speed Profile

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Hull Model Engine Model Propeller Model

+

Trim, Displacement, Wind, Waves, ...

Fuel Consumption Model Other Models Fuel Consumption Speed, Weather, Displacement, Currents, Trim, ...

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Fuel Consumption Model

  • Predetermined equations
  • Prior information
  • Understandable
  • Rigid
  • Not adjusting to changing

conditions

White Box

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Fuel Consumption Model

  • Loosely predetermined
  • No prior information
  • Hard to understand
  • Flexible

Black Box

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Fuel Consumption Model

  • Partly predetermined
  • Prior information
  • Easy to understand
  • Flexible
  • Adjusts to changes

Grey Box

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Collected Data

Speed through water Wind direction & velocity, RPM, Torque, Trim, List, Draft, Rolling, Surging, Pitching, GPS Position ...

Sample Frequency: 1-16Hz ~4,000,000 measurements / day

Mathematical Model

Hydrodynamic model Aerodynamic model Propulsion model Engine model

Statistical Analysis Fuel Consumption Model Fuel Consumption Model

Given these inputs, what is the estimate for fuel consumption?

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Fuel Consumption Model

White Box Grey Box

Example: Squatting

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White Box Grey Box

Fuel Consumption Model

Example: Wind Resistance

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Ingredients of Optimization

Fuel Consumption Model Operating Conditions Constraints Optimization Algorithm Optimal Speed Profile

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Optimization Algorithm

  • OpenMDAO (Python)
  • Quality of inputs is

critical for

  • ptimization
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SPEED

THROUGH WATER

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CHALLENGES IN SPEED OPTIMIZATION

  • ETA estimation: changing conditions
  • Hull, Propeller & Engine: off-design & degradation
  • Weather forecasts: reliability, accuracy
  • Obtainable service speed: changing conditions
  • Hydrodynamic and aerodynamic modelling: accuracy
  • Data: quality, sufficiency
  • Frequent speed adjustments
  • End-user acceptance: ease-of-use, involvement
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THE HUMAN FACTOR

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Fuel Consumption Model Operating Conditions Constraints Optimization Algorithm Optimal Speed Profile

THE HUMAN FACTOR

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Rate-of-Usage Report (ROU) Dynamic Sea Margin

Eniram delivered reports

Hull Fouling Analysis Energy Reporting

Eniram Fleet Performance Manager

KPIs

THE HUMAN FACTOR

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THANK YOU! ANY QUESTIONS?