Analysing the Data Peter Mantel, Managing Director 05th November 2014 BMT Group - History BSRA NMI Ltd British Ship Research Association (1887) National Maritime Institute (1909) British Maritime Technology Ltd established 1985 (now BMT Group Ltd) © BMT SMART Ltd. 2014 Analysing the Data | 2 BMT Group £156 million turnover 1,400 consultants Research led © BMT SMART Ltd. 2014 Global clients BMT Smart Briefingthe|Data 3 Analysing | 3 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 | 4 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 Analysing the Data | 5 How to measure vessel performance 1 Vessel performance data is automatically collected on-board as your advanced BMT SMART solution interfaces with systems and sensors. 2 Panel displays and on-board computers can be used to present key trending information and live feedback continually to the crew. 3 Satellite communications are used to automatically relay the vessel’s performance data ashore while also updating the on-board system. 4 All of this information is stored securely on our servers, where it is modelled with our high-quality Metocean data. 5 Our web-based platform provides easy and intuitive access to manage and analyse vessel and fleet performance. 3 2 1 4 5 © BMT SMART Ltd. 2014 Analysing the Data | 6 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 In this presentation we discuss the analysis step further. © BMT SMART Ltd. 2014 Analysing the Data | 7 Analysis techniques • There are two accepted methods for preparing vessel performance data for analysis: 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. Normalisation Raw Performance Data Prepared Performance Data Analysis and Trending Filtering • 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. © BMT SMART Ltd. 2014 Analysing the Data | 8 What is normalisation? Model test baseline 25000 20000 In service data Power (MW) 15000 10000 5000 0 0 5 10 15 20 Speed (kts) Model Tests Base Condition Input Dataset Before Normalising After Normalising © BMT SMART Ltd. 2014 Analysing the Data | 9 What is normalisation? • Normalisation relies solely on a full and accurate model to avoid any uncertainty introduced to the analysis. 25000 20000 • • 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. 15000 Power (MW) • 10000 5000 0 0 5 10 15 Speed (kts) Model Tests Base Condition © BMT SMART Ltd. 2014 Input Dataset Over corrected 20 data Corrected Dataset Analysing the Data | 10 Why filtering? Filtered for: • Draught • Wind Speed • Wave Height • Current Speed Before Filtering • After Filtering Filtering requires a large dataset to ensure that after filtering there is still sufficient data to produce reliable analysis. © BMT SMART Ltd. 2014 Analysing the Data | 11 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 | 12 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 © BMT SMART Ltd. 2014 The SFOC, Propeller and Hull Coefficients: Isolate the individual components giving the efficiency of each independently Analysing the Data | 13 The importance of sensor accuracy Studies show that the precision of the speed sensor is fundamental to reducing uncertainty in analysis. Performance Indicator Bias 2000 MC Simulation: Average Performance Indicator, kW • 1500 1000 500 0 -500 -1000 -1500 -2000 Performance indicator bias, L. Aldous et al. (2014) The error bars indicate the precision, the redline is the performance indicator of the baseline © BMT SMART Ltd. 2014 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. 25 20 20 SOG - tides - ocean (kn) 25 15 SOG (kn) • 10 5 0 -5 10 5 0 0 -5 15 5 10 15 20 25 -5 0 -5 Log speed (kn) © BMT SMART Ltd. 2014 5 10 15 20 25 Log speed (kn) Analysing the Data | 15 Comparing data collection techniques Changes to Input Uncertainties Effect of Input Uncertainties on CM and NR baselines for Different Evaluation Periods 4 20 Model error 27 5 146 24 Full days 44 12 211 62 Baseline 50 0 50 255 100 150 200 250 300 Uncertainty as a Percentage of the change in Ship Performance CM 9 months CM 3 months NR 9 months NR 3 months Simulation uncertainty sensitivity analysis results, L. Aldous et al. (2014) • 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. © BMT SMART Ltd. 2014 Analysing the Data | 16 Changes to Input Uncertainti 20 Model error 27 Comparing data collection techniques 5 146 24 Full days 44 12 211 62 Baseline 50 0 50 255 100 150 200 250 300 Uncertainty as a Percentage of the change in Ship Performance CM 9 months • NR 9 months NR 3 months 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 • CM 3 months ≈ 9 months Noon Reporting This shows that there is a significant benefit in continuous monitoring for reducing uncertainty in data. © BMT SMART Ltd. 2014 Analysing the Data | 17 What to measure? • • • For a vessel, the most complete measure of efficiency is the relationship between vessel speed and shaft power or fuel consumption. Shaft Power vs Log Speed Fuel Consumption vs Log Speed 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. © BMT SMART Ltd. 2014 Analysing the Data | 18 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 | 19 What data is required? Vessel Data • • • • • • • • • • • • • Fuel Consumption (Mass) Fuel Quality (Calorific Value) Shaft Power Metocean Data • • • Wind Speed Wave Height Current Speed Speed Through Water Draught Trim Propeller RPM Propeller Pitch (CPP vessels) Water Temperature Water Salinity Speed Over Ground 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 Heading Water Depth © BMT SMART Ltd. 2014 Analysing the Data | 20 Data acquisition Marine approved Bridge 15” Touchscreen Display Ship’s Network Ship’s Power Windows 7 SSD PC ECDIS GPS Modular 10 year parts guarantee Echo Sounder Gyro 24v - UPS Speed Log Rudder Angle 8 x Serial Interface VDR RS232/422/485 Dual LAN For Redundancy IAS/EMS ECR/CCR 24 x Analogue Interface M/E In Mass Flow e.g. 0-10V, 4-20mA 1 x Serial D/G Interface In Volume Flow RS232/422/485 D/G Out Volume Flow Shaft Power Shaft RPM Shaft Torque Draught FWD Fuel Temperature Draught AFT Tank Levels Propeller Pitch Trim © BMT SMART Ltd. 2014 Analysing the Data | 21 Vessel View © BMT SMART Ltd. 2014 Analysing the Data | 22 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 | 23 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 | 24 Analysis examples: Dry-docking Dry-docking • VLCC Tanker before and after dry-dock. © BMT SMART Ltd. 2014 Analysing the Data | 25 Carbon Credits : A Marine Industry First 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 All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale. © BMT SMART Ltd. 2014 Analysing the Data | 26 Carbon Credits : A Marine Industry First 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 CO2 saved = 1 carbon credit All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale. © BMT SMART Ltd. 2014 Analysing the Data | 27 The Methodology 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® CO2 emissions as a function of Speed • The same data source is then used to determine the emission savings after the application of Intersleek ® Baseline Data from first dock cycle Daily CO2 Saving Data from first year of Intersleek® application Time (days since dock) All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale. © BMT SMART Ltd. 2014 Analysing the Data | 28 Vessel Eligibility 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) All products supplied and technical advice or recommendations given are subject to our standard Conditions of Sale. © BMT SMART Ltd. 2014 Analysing the Data | 29 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 Analysing the Data | 30 Thank you. Any questions?