monohakobi technology institute how ict can assist energy efficient fleet operations -how broadband...

MonohakobiTechnology InstituteMonohakobiTechnology Institute

How ICT Can Assist Energy Efficient Fleet Operations

-How Broadband Changes Quality of Weather Routing

Digital Ship Singapore 22-23, May, 2012

Ryo KakutaTechnical Strategy Group, MTI (Monohakobi Technology

Institute)R&D company of NYK Line


Outline• Background

• Energy Efficient Fleet Operations

• Optimum Weather Routing

• Weather Routing and Monitoring

• Improvement of Weather Routing by Broadband

• Next Challenge

2


Background


Save bunker activities in shipping company• According to increased cost of bunker, shipping companies have applied

operational and technical measures for fuel savings– Slow steaming– Weather routing– Propeller cleaning– Energy saving devices

Cost benefit and emission reduction by slow steaming

e.g. 8,000 TEU containerShip speed 24 knot 20 knot

M/E fuel consumption

225 ton/day 130 ton/day

M/E fuel cost(@ 600 USD/MT)

134,800 USD/day 78,000 USD/day

CO2 emission 696 ton/day 403 ton/day

Slow steaming

- 42 %

- 16 %

4


Performance monitoring- compare total fuel consumption

• Same ship size and same voyage – but total amounts of fuel consumption largely differ

Comparison of total fuel consumption per voyageSame ship size and same voyage

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

A -1 B-1 C-1 D-1 E-1 F-1 G-1 A-2 B-2 C-2 D-2 F-2 G-2

Vessel - Voyage

Fuel

Con

sum

ptio

n [M

T]

More than 30 % difference

5


SEEMP - PDCA management for energy efficiency

• SEEMP (Ship Energy Efficiency Management Plan)– MEPC 62 adopted revisions of MARPOL Annex VI introducing EEDI and SEEMP

• Entry into force date: 1 January 2013

EEOI trend

0

1

2

3

4

5

6

Voy. 1 Voy. 2 Voy. 3 Voy. 4 Voy. 5 Voy. 6 Voy. 7 Voy. 8

Voyage number

EEO

I [g

/ton

-mile

]Operational measures

• slow steaming

• weather routing

• hull and propeller maintenance

….

Plan Do Check Act

Continuous monitoring & improvement

6Importance of Energy Efficient Operation is increasing


Energy Efficient Fleet Operations


Fleet operation

Snapshot from NYK e-Missions’NYK fleet: about 800 vessels now

• Best balance of safety, economy and environment– No cargo and ship damage– Keep schedule– Maximize charter base (minimize cost)– Minimize emissions

8Multi-objective optimization


Management for energy efficient operation- Needs all related parties participation

Do - navigation

Check – monitoring

Plan – routing

PDCA cycle forimprovement

To encourage all participants efforts for energy efficient operation by sharing information, good communication and right scheme

9

Act – corrective action


Challenge to Optimize Fleet Operation in NYK

10

Figure from “More Than Shipping 2013”

Monitoring

Weather Routing

Broadband

Real-time Operation

Concept to Realization!


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Introduction of Onboard Broadband on NYK Fleet- Improve Infrastructure

Reducing CO2 emissions by introducing onboard broadband


IBIS (Innovative Bunker and Idle-time Saving) PJ- Effective Utilization of Broadband

Sharing Information including weather, sea forecasts, sea-current,and ship-operation data between land and ships in real time.


Optimum Weather Routing


Optimum Weather Routing• Role of weather routing

– (past) Avoiding severe weather– (now) Optimum weather routing

Best balance of •Safety•Schedule keep•Economy•Environment

• Necessary technology for optimum weather routing– Ship performance model

•RPM – speed – fuel consumption– Ship motion and performance in severe

weather

Way points

Routes and weather

14


15

Optimum Weather Routing-Necessity of Ship Performance Model

“Courtesy of WNI”


Weather Routing and Monitoring


Integration of Weather Routing with Monitoring

Weather Routing （ PLAN ）

• Voyage plan

+ weather forecast

+ ship performance model

+ ship motion model

Performance Monitoring （ CHECK ）

• Actual voyage

+ actual weather

+ ship performance data

+ ship motion dataFeedback

Ship model and weather forecast inherently include errors.

Feedback loop with monitoring can make this system work better.

17


Example Implementation of Data Collection box Onboard

• Requirements

• Interface to onboard equipment, such as engine D/L, GPS, anemometer, flow meter and etc.

• High reliability … 24 hrs, 365 days work without maintenance

• Lower cost of implementation

• Flexibility of customization

Flow meter

FUELNAVI18


Engine Data Logger

GPS (speed, course) NMEA

Doppler log (speed) NMEA

Gyro compass (heading) NMEA

Anemometer (rel. wind) NMEA

RPM 4-20 mA

F.O. flow meter pulse

S.H.P 4-20 mA

Master clock pulse

F.O. temperature 4-20 mA

Sea water temp. 4-20 mA

E/R temp. 4-20 mA

serial / LAN

GOT monitor-Fuel consumptionmonitor

serial / LAN

Ship’s LAN

Inmarsat-FBBor VSAT

MotionSensor serial

FUELNAVI Schematic Diagram

Bridge

E/C

Box Computer(MOXA)-data storage-data transfer

FuelNavi

(PLC: MitsubishiMELSEC-Q)

-Data processing-Calculate statistics

SIMS junction box

serial

19


SIMS Overview(Ship Information Management System)

VDR / ECDIS

Engine Data Logger

Data Acquisition and Processing

SIMS Viewer

-Trend monitoring of speed, M/E RPM, fuel consumption and other conditions per hour

- Comparing planned schedules and actual schedules• Main Engine

• FO flow meter

• Torque meter

• GPS

• Doppler log

• Anemometer

• Gyro Compass

Inmarsat-F/FB

<Navigation Bridge>

<Engine Room>

Viewer

Motion sensor

Data Center

SIMS Monitoring & Analysis System at Shore

Operation Center

Singapore, ….

Technical Analysis (MTI)

Voyage Analysis Report Break down analysis of fuel consumption for each voyage

Feedback to captains

SIMS Data Collection System Onboard

Report

SIMS auto logging data (per hour) & SPAS electronic

abstract logbook data (per day)

Communications via Technical Management

FuelNavi

20

Weather routing service provider


Optimum Weather Routing

Performance Model

Weather Forecast

Voyage Planning

Noon Report

RPM

Spe

ed

2.Model Calibration

SIMS Data

1.Model Improvement

RPM

Spe

ed

Calibrate Model based onActual data

Good Performance Model basedon actual and detail data

SIMS Data

SIMS DataCOmmunication

SIMS Data

Noon Rpt.

Before Model

After Ajustment

Real Data

4.Evaluation

Feedback to WeatherRouting

Feedback

3.MonitoringMonitoring Gap between

Actual and plan

SIMS Data

Weather RoutingVessel

OperationCom

munica

tion

Comm

unication

Communication

FOC Safety SchedulePlan APlan BPlan C

L

78 rpm 82 rpm

Route and RPM

82 rpm

Recommendation

AfterDeparture

PreVoyage

DuringNavigation

PostVoyage

SIMS Data

Integrating Optimum Weather Routing with SIMS


0

20

40

60

80

100

120

140

160

180

0 20 40 60 80 100

Report

New

Old

0

100

200

300

400

500

600

700

800

900

-50 -40 -30 -20 -10 0 10 20 30 40 50

New

Old

RPM Model – Actual [mt/24h]

FOC

[mt/

24h]

Freq

uenc

y

Container Ship Sample

Standard deviation reduces from 9.3[mt/24h] to 5.4[mt/24h].Estimation accuracy improves about 40%.

Zero error peak enhancementshows accuracy improvement.

σ(old) = 9.3[mt/24h]σ(new) = 5.4[mt/24h]

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Performance Model Correction(Pre-voyage)



Improvement of Weather Routing by Broadband


Improvement of Weather Routing by Broadband

rpm

spe

ed

　　

Calibrationmodel

Calibrated model

actual

Maritime broadband (FBB, VSAT)

Revise schedule by real-time information

15 days forecast 1/12 resolution current

Voyage simulation

onboard

vesselCaptain and engineer at shore

Recommend RPM

Actual RPM

Recommend speed

Feedback to ship performance model

Full time connection

Large data size transfer

Voyage simulation

shore

Feedback actual weather

Actual sea state

Actual wind & ship motion

24


Rich Weather Content by Fleet Broadband

25

Narrowband Broadband

Frequency 2 times/day 4 times/day

Forecast Range 10days 15days

Grid Size (Current) 1/2 degree 1/32 degree

Current (1/2 degree)

Hi-Resolution Current (1/32 degree)“Courtesy of WNI”


26

Error Monitoring Error Minimization

Error from Ab-Log analysis orPast performance

● Reported FOC● Simulated FOC(WNI)

● Reported FOC● Simulated FOC(WNI)

Semi-autoCalibration

Vessel Performance DB Voyage Plan

VoyageRecords

Simulation Settingbased on the similarvoyage recodes

Error becomes small

About 5mt under-estimation

All of datawithin ±2.5mtdifference

This processcan be applied forBROB-differenceor M/E FOC.Estimation of totalFOC is improved.

Underwayprocess

Real-time Performance Model Correction



Next Challenge


Further Improvement

Uncertainties

Weather Forecast

Ship Performance Ship Motion

Continuous effort is requiredfor reducing uncertainties in weather routing-Reducing gap between estimate and actual- Monitoring and feedback

Uncertainties


Compare Estimated Ship Performance with Actual

Comparison

ActualEstimate

Ship Performance Model Actual Performance and Weather

Weather Forecast

Wave Height Meter

Measuring actual wave height remains a challenge.


Compare Optimum Trim Estimation with Actual

30

Optimum trim estimation(reasoning by model test, simulation)

Trim trial with performance monitoring

Comparison

The relation of propulsive performance and trim are physically complex problem.


Compare Estimated Ship Motion with Actual

ship motion simulation actual ship motion and acceleration

cargo securing & ship structural safety

criteria

31

[sec]


Expectation on Broadband

Weather Routing

Enhancement of monitoring plays a key roll to improve weather routing.Installation of broadband will accelerate the cycle of improvement.

Improvement

Ship performance, ship motion, draft and trim, wave height,,,,,,,,,,,,,,


Concluding remarks

• NYK aims to optimize fleet operation by integrating weather routing, monitoring and broadband

• Installation of broadband enables sending rich weather content to vessels and real-time weather routing

• For reducing uncertainties in weather routing, the cycle of estimation, monitoring and feedback is required

• Broadband will contribute to acceleration of the cycle

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