Monohakobi Technology Institute How ICT Can Assist Energy

Monohakobi Technology Institute How ICT Can Assist Energy Efficient Fleet Operations -How Broadband Changes Quality of Weather Routing Digital Ship Singapore 22 -23, May, 2012 Ryo Kakuta Technical Strategy Group, MTI (Monohakobi Technology Institute) R&D company of NYK Line

Monohakobi Technology Institute Outline • Background • Energy Efficient Fleet Operations • Optimum Weather Routing • Weather Routing and Monitoring • Improvement of Weather Routing by Broadband • Next Challenge 2

Monohakobi Technology Institute Background

Monohakobi Technology Institute 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 container 4 Slow steaming Ship 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 CO 2 emission 696 ton/day 403 ton/day - 16 % - 42 %

Monohakobi Technology Institute Performance monitoring - compare total fuel consumption More than 30 % difference 5

Monohakobi Technology Institute 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 Operational measures • slow steaming • weather routing • hull and propeller maintenance …. Plan Do Check Act Continuous monitoring & improvement Importance of Energy Efficient Operation is increasing 6

Monohakobi Technology Institute Energy Efficient Fleet Operations

Monohakobi Technology Institute Fleet operation • Best balance of safety, economy and environment – No cargo and ship damage – Keep schedule – Maximize charter base (minimize cost) – Minimize emissions 8 Multi-objective optimization Snapshot from NYK e-Missions’ NYK fleet: about 800 vessels now

Monohakobi Technology Institute Management for energy efficient operation - Needs all related parties participation Plan – routing Do - navigation PDCA cycle for improvement Act – corrective action Check – monitoring To encourage all participants efforts for energy efficient operation by sharing information, good communication and right scheme 9

Monohakobi Technology Institute Challenge to Optimize Fleet Operation in NYK Real-time Operation Monitoring Weather Routing Broadband Figure from “More Than Shipping 2013” Concept to Realization! 10

Monohakobi Technology Institute Introduction of Onboard Broadband on NYK Fleet - Improve Infrastructure Reducing CO 2 emissions by introducing onboard broadband 11

Monohakobi Technology Institute 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 ships in real time.

Monohakobi Technology Institute Optimum Weather Routing

Monohakobi Technology Institute 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 14 severe weather Way points Routes and weather

Monohakobi Technology Institute Optimum Weather Routing -Necessity of Ship Performance Model 15 “Courtesy of WNI”

Monohakobi Technology Institute Weather Routing and Monitoring

Monohakobi Technology Institute Integration of Weather Routing with Monitoring Performance Monitoring（CHECK） Weather Routing（PLAN） • Actual voyage • Voyage plan + actual weather + weather forecast + ship performance model + ship motion model Feedback + ship performance data + ship motion data Ship model and weather forecast inherently include errors. 17 Feedback loop with monitoring can make this system work better.

Monohakobi Technology Institute Example Implementation of Data Collection box Onboard • Requirements Flow meter • 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 18 FUELNAVI

Monohakobi Technology Institute Inmarsat-FBB or VSAT Technology Institute FUELNAVI Schematic Diagram Ship’s LAN SIMS junction box Motion Sensor serial Box Computer (MOXA) -data storage -data transfer serial GPS (speed, course) NMEA Doppler log (speed) Gyro compass (heading) NMEA Anemometer (rel. wind) Bridge Fuel. Navi NMEA (PLC: Mitsubishi MELSEC-Q) NMEA serial / LAN -Data processing -Calculate statistics serial / LAN E/C Engine Data Logger RPM 4 -20 m. A F. O. flow meter S. H. P Master clock pulse 4 -20 m. A F. O. temperature 19 Sea water temp. E/R temp. 4 -20 m. A pulse 4 -20 m. A GOT monitor -Fuel consumption monitor

Monohakobi Technology Institute Data Center Technology Institute SIMS Overview (Ship Information Management System) Weather routing service provider SIMS auto logging data (per hour) & SPAS electronic abstract logbook data (per day) SIMS Data Collection System Onboard SIMS Monitoring & Analysis System at Shore Inmarsat-F/FB Communications via Technical Management Feedback to captains Operation Center • GPS Singapore, …. • Doppler log • Anemometer Fuel. Navi Report • Gyro Compass Viewer Voyage Analysis Report Break down analysis of fuel consumption for each voyage SIMS Viewer VDR / ECDIS -Trend monitoring of speed, M/E Data Acquisition and Processing <Navigation Bridge> Technical Analysis (MTI) <Engine Room> • Main Engine • FO flow meter • Torque meter 20 RPM, fuel consumption and other conditions per hour Motion sensor Engine Data Logger - Comparing planned schedules and actual schedules

Monohakobi Technology Institute Integrating Optimum Weather Routing with SIMS Feedback Optimum Weather Routing Post Voyage 4. Evaluation Weather Forecast Feedback to Weather Routing n Voyage Planning un i ca tio Operation m m Co m m SIMS Data Co COmmunication un Recommendation n tio ica Performance Model 82 rpm 78 rpm 82 rpm SIMS Data L 3. Monitoring Route and RPM Weather Routing Communication Vessel Monitoring Gap between Actual and plan SIMS Data Noon Rpt. RPM Good Performance Model based on actual and detail data After Departure 2. Model Calibration Before Model After Ajustment Speed 1. Model Improvement Speed Pre Voyage During Navigation Noon Report Real Data RPM Calibrate Model based on Actual data SIMS Data

Monohakobi Technology Institute Performance Model Correction(Pre-voyage) Zero error peak enhancement shows accuracy improvement. Frequency FOC [mt/24 h] Container Ship Sample RPM σ(old) = 9. 3[mt/24 h] σ(new) = 5. 4[mt/24 h] Model – Actual [mt/24 h] Standard deviation reduces from 9. 3[mt/24 h] to 5. 4[mt/24 h]. Estimation accuracy improves about 40%. “Courtesy of WNI” 22

Monohakobi Technology Institute Improvement of Weather Routing by Broadband

Monohakobi Technology Institute Improvement of Weather Routing by Broadband 15 days forecast 1/12 resolution current Voyage simulation shore onboard Large data size transfer Maritime broadband (FBB, VSAT) Revise schedule by realtime information Actual RPM Recommend speed 24 Full time connection vessel Feedback 　　to ship performance model Feedback actual weather Actual sea state Actual wind & ship motion speed Captain and engineer at shore Calibration model Calibrated model actual rpm

Monohakobi Technology Institute Rich Weather Content by Fleet Broadband Narrowband Broadband Frequency 2 times/day 4 times/day Forecast Range 10 days 15 days Grid Size (Current) 1/2 degree 1/32 degree Current (1/2 degree) “Courtesy of WNI” Hi-Resolution Current (1/32 degree) 25

Monohakobi Technology Institute Real-time Performance Model Correction Vessel Performance DB Voyage Records Voyage Plan Simulation Setting based on the similar voyage recodes Error Monitoring Underway process Error Minimization Semi-auto Calibration Error from Ab-Log analysis or Past performance ● Reported FOC ● Simulated FOC(WNI) About 5 mt underestimation This process can be applied for BROB-difference or M/E FOC. Estimation of total FOC is improved. Error becomes small ● Reported FOC ● Simulated FOC(WNI) All of data within ± 2. 5 mt difference 26 “Courtesy of WNI”

Monohakobi Technology Institute Next Challenge

Monohakobi Technology Institute Further Improvement Weather Forecast Ship Performance Ship Motion Uncertainties Continuous effort is required for reducing uncertainties in weather routing - Reducing gap between estimate and actual - Monitoring and feedback Uncertainties

Monohakobi Technology Institute Compare Estimated Ship Performance with Actual Ship Performance Model Actual Performance and Weather Comparison Weather Forecast Estimate Actual Measuring actual wave height remains a challenge. Wave Height Meter

Monohakobi Technology Institute Compare Optimum Trim Estimation with Actual Comparison Optimum trim estimation (reasoning by model test, simulation) Trim trial with performance monitoring The relation of propulsive performance and trim are physically complex problem. 30

Monohakobi Technology Institute Compare Estimated Ship Motion with Actual [sec] ship motion simulation criteria actual ship motion and acceleration cargo securing & ship structural safety 31

Monohakobi Technology Institute Expectation on Broadband Feedback Estimation Weather Routing Improvement Monitoring Ship performance, ship motion, draft and trim, wave height, , , , Enhancement of monitoring plays a key roll to improve weather routing. Installation of broadband will accelerate the cycle of improvement.

Monohakobi Technology Institute 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 33