05 th November 2014 Analysing the Data Peter Mantel, Managing Director

BMT Group - History BSRA British Ship Research Association (1887) British Maritime Technology Ltd established 1985 (now BMT Group Ltd) NMI Ltd National Maritime Institute (1909) Analysing the Data | 2 © BMT SMART Ltd. 2014

BMT Smart Briefing | 3 £156 million turnover1,400 consultants Research led Global clients BMT Group Analysing the Data | 3 © BMT SMART Ltd. 2014

Analysing the Data | 4 Introduction 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

Analysing the Data | 5 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. © BMT SMART Ltd. 2014

Vessel performance data is automatically collected on-board as your advanced BMT SMART solution interfaces with systems and sensors Analysing the Data | 6 How to measure vessel performance 1 Panel displays and on-board computers can be used to present key trending information and live feedback continually to the crew. 2 Satellite communications are used to automatically relay the vessel’s performance data ashore while also updating the on-board system. 3 All of this information is stored securely on our servers, where it is modelled with our high-quality Metocean data. 4 Our web-based platform provides easy and intuitive access to manage and analyse vessel and fleet performance. 5 © BMT SMART Ltd. 2014

Analysing the Data | 7 How to understand vessel performance 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 © BMT SMART Ltd In this presentation we discuss the analysis step further.

Analysing the Data | 8 Analysis techniques There are two accepted methods for preparing vessel performance data for analysis: Filtering Raw Performance Data Normalisation Prepared Performance Data Analysis and Trending BMT’s approach is to automate the collection of data to allow for a sufficiently large dataset to filter. We believe that the risks of correcting the data through normalisation may lead to misinterpretation. 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. © BMT SMART Ltd Filtering

Analysing the Data | 9 What is normalisation? Before NormalisingAfter Normalising In service data Model test baseline © BMT SMART Ltd. 2014

Analysing the Data | 10 What is normalisation? Normalisation relies solely on a full and accurate model to avoid any uncertainty introduced to the analysis. 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. Over corrected data © BMT SMART Ltd. 2014

Analysing the Data | 11 Why filtering? Filtering requires a large dataset to ensure that after filtering there is still sufficient data to produce reliable analysis. Before FilteringAfter Filtering Filtered for: Draught Wind Speed Wave Height Current Speed © BMT SMART Ltd. 2014

Analysing the Data | 12 What do we do? 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. © BMT SMART Ltd. 2014

Analysing the Data | 13 BMT’s approach to analysis - Coefficients 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 The SFOC, Propeller and Hull Coefficients: Isolate the individual components giving the efficiency of each independently © BMT SMART Ltd. 2014

The importance of sensor accuracy Studies show that the precision of the speed sensor is fundamental to reducing uncertainty in analysis. © BMT SMART Ltd Performance indicator bias, L. Aldous et al. (2014) The error bars indicate the precision, the redline is the performance indicator of the baseline Analysing the Data | 14

The importance of accurate speed through water Using Speed over Ground instead of Speed through Water can have a dramatic effect on uncertainty. Speed Log sensor drift has a significant effect on performance indicators. 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. © BMT SMART Ltd Analysing the Data | 15

Comparing data collection techniques 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. Simulation uncertainty sensitivity analysis results, L. Aldous et al. (2014) Analysing the Data | 16 © BMT SMART Ltd. 2014

Comparing data collection techniques 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 9 months Noon Reporting ≈ This shows that there is a significant benefit in continuous monitoring for reducing uncertainty in data. © BMT SMART Ltd Analysing the Data | 17

Analysing the Data | 18 What to measure? For a vessel, the most complete measure of efficiency is the relationship between vessel speed and shaft power or fuel consumption. 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. Shaft Power vs Log Speed Fuel Consumption vs Log Speed © BMT SMART Ltd. 2014

Analysing the Data | 19 The Power Coefficient 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. © BMT SMART Ltd. 2014

Analysing the Data | 20 What data is required? © BMT SMART Ltd Vessel Data Fuel Consumption (Mass) Fuel Quality (Calorific Value) Shaft Power Speed Through Water Draught Trim Propeller RPM Propeller Pitch (CPP vessels) Water Temperature Water Salinity Speed Over Ground Heading Water Depth Metocean Data Wind Speed Wave Height Current Speed Vessel Particulars 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

Data acquisition © BMT SMART Ltd v - UPS Ship’s Network Dual LAN For Redundancy ECDIS GPS Echo Sounder Gyro Speed Log Rudder Angle VDR IAS/EMS Bridge ECR/CCR Propeller Pitch Shaft Power Shaft RPM Draught FWD Draught AFT Shaft Torque Fuel Temperature M/E In Mass Flow D/G In Volume Flow D/G Out Volume Flow Tank Levels Trim Ship’s Power 15” Touchscreen Display Windows 7 SSD PC 8 x Serial Interface RS232/422/ x Analogue Interface e.g. 0-10V, 4-20mA 1 x Serial Interface RS232/422/485 Marine approved Modular 10 year parts guarantee Analysing the Data | 21

Analysing the Data | 22 Vessel View © BMT SMART Ltd. 2014

Analysing the Data | 23 Analysis examples: Stationary period and hull scrub Stationary period and hull scrub LNG Tanker operating in the Middle East. © BMT SMART Ltd. 2014

Analysing the Data | 24 Analysis examples: Effect of New Coating Stationary period and hull scrub LNG Tanker operating in the Middle East. © BMT SMART Ltd. 2014

Analysing the Data | 25 Analysis examples: Dry-docking Dry-docking VLCC Tanker before and after dry-dock. © BMT SMART Ltd. 2014

Analysing the Data | 26 © BMT SMART Ltd Carbon Credits : A Marine Industry First All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale. 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

Analysing the Data | 27 © BMT SMART Ltd Carbon Credits : A Marine Industry First All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale. 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 CO 2 saved = 1 carbon credit

Analysing the Data | 28 © BMT SMART Ltd The Methodology All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale. 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 ® The same data source is then used to determine the emission savings after the application of Intersleek ® Time (days since dock) CO 2 emissions as a function of Speed Baseline Data from first dock cycle Data from first year of Intersleek ® application Daily CO 2 Saving

Analysing the Data | 29 © BMT SMART Ltd Vessel Eligibility All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale. 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)

Analysing the Data | 30 Summary 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 © BMT SMART Ltd. 2014

Thank you. Any questions?