A novel systematic methodology for ship propulsion engines energy management

Abstract Establishing an energy monitoring and management methodology is a quintessential milestone for informed energy savings decision making as well as for effectively reducing the cost and the environmental impact of shipping operations. In this study, a novel systematic methodology is proposed for the energy management of the ship propulsion engine, which is the largest ship energy producer. The methodology employs a combination of tools including statistical analysis, compressor modelling to predict the engine airflow and energy and exergy analyses, whereas its output includes the engine operating profile, the most frequently occurring propeller curves and the engine most frequent operating points, the key performance indicators for quantitatively assessing the recorded parameters quality as well as the energy/exergy flows and the engine components exergy destruction. The methodology is implemented for three case studies for a very large crude carrier, a container ship and a bulk carrier, for which recorded data were available by using different monitoring techniques from either noon reports or automatic data acquisition systems. The derived results provide the engine operating profile demonstrating that the investigated vessels were operating in slow steaming conditions with a lower engine efficiency associated with a greater exhaust gas wasted energy. The measured data analysis demonstrates the better quality of the data recorded by automated monitoring systems and pinpoint maintenance issues of the engine components. Lastly, the exergy analysis results demonstrate that the exhaust gas and jacket cooling water provide the greater exergy flows rendering them attractive for energy saving initiatives, whereas the engine block, compressor and turbine are the engine components with the greater exergy destruction, thus requiring closer monitoring for timely identifying mitigating measures.

[1]  H. Johnson,et al.  The logic of business vs. the logic of energy management practice: understanding the choices and effects of energy consumption monitoring systems in shipping companies , 2016 .

[2]  Davide Anguita,et al.  Vessels fuel consumption forecast and trim optimisation: A data analytics perspective , 2017 .

[3]  Ole Winther,et al.  Statistical modelling for ship propulsion efficiency , 2012 .

[4]  Charlotte Banks,et al.  Integrated approach to vessel energy efficiency , 2015 .

[5]  Susan W. Stewart,et al.  The Usefulness of Entropic Average Temperatures , 2004 .

[6]  Kevin P. Logan Using a Ship's Propeller for Hull Condition Monitoring , 2011 .

[7]  Gerasimos Theotokatos,et al.  Dynamic energy modelling for ship life-cycle performance assessment , 2015 .

[8]  Jill Carlton,et al.  Marine Propellers and Propulsion , 2007 .

[9]  Osman Turan,et al.  An application of fuzzy-AHP to ship operational energy efficiency measures , 2016 .

[10]  Haibin Wang,et al.  An effective framework for life cycle and cost assessment for marine vessels aiming to select optimal propulsion systems , 2018 .

[11]  Peilin Zhou,et al.  Life cycle assessment as a complementary utility to regulatory measures of shipping energy efficiency , 2016 .

[12]  S. C. Kaushik,et al.  Estimation of chemical exergy of solid, liquid and gaseous fuels used in thermal power plants , 2013, Journal of Thermal Analysis and Calorimetry.

[13]  Artur Gramacki,et al.  Nonparametric Kernel Density Estimation and Its Computational Aspects , 2017 .

[14]  P. Marty Étude de l'efficacité énergétique des navires : développement et application d'une méthode d'analyse , 2014 .

[15]  Ulrik Dam Nielsen,et al.  Fault Detection for Shipboard Monitoring Volterra Kernel and Hammerstein Model Approaches , 2009 .

[16]  Lars Nielsen,et al.  Turbocharging Basics and Models , 2014 .

[17]  Gerasimos Theotokatos,et al.  A computational study on the performance and emission parameters mapping of a ship propulsion system , 2015 .

[18]  José G. Ramcrez Data Analysis: Statistical and Computational Methods for Scientists and Engineers , 2000, Technometrics.

[19]  Sepideh Jafarzadeh,et al.  A framework to bridge the energy efficiency gap in shipping , 2014 .

[20]  Mikael Johansson,et al.  Will the ship energy efficiency management plan reduce CO2 emissions? A comparison with ISO 50001 and the ISM code , 2013 .

[21]  Xinping Yan,et al.  Real-time optimization of ship energy efficiency based on the prediction technology of working condition , 2016 .

[22]  Stéphane Bressan,et al.  A framework for real-time monitoring of energy efficiency of marine vessels , 2018 .

[23]  Nestor Goicoechea,et al.  Adapting the shipping sector to stricter emissions regulations: Fuel switching or installing a scrubber? , 2017 .

[24]  Thomas Bauernhansl,et al.  Methodology for Energy Efficiency on Process Level , 2013 .

[25]  Francesco Baldi,et al.  Energy and exergy analysis of ship energy systems - the case study of a chemical tanker , 2015 .

[26]  Panos Deligiannis Ship performance indicator , 2017 .

[27]  Lars Eriksson,et al.  Parameterizing Compact and Extensible Compressor Models Using Orthogonal Distance Minimization , 2017 .

[28]  Kenneth R. Currie,et al.  Specification of energy assessment methodologies to satisfy ISO 50001 energy management standard , 2017 .

[29]  Linda Styhre,et al.  Increased energy efficiency in short sea shipping through decreased time in port , 2015 .

[30]  Burkhard Heer,et al.  Dynamic General Equilibrium Modeling: Computational Methods and Applications , 2005 .

[31]  George G. Dimopoulos,et al.  A general-purpose process modelling framework for marine energy systems , 2014 .

[32]  S. Sheather Density Estimation , 2004 .

[33]  Hanna Barbara Rasmussen,et al.  Energy efficiency of working vessels – A framework , 2017 .

[34]  Francesco Di Maria,et al.  On line measurement of the lower heating value of waste and energetic efficiency of an existing waste to energy plant: Identification of uncertainty associated to probes and their influence on the results , 2017 .

[35]  Richard Bucknall,et al.  Uncertainty analysis in ship performance monitoring , 2015 .

[36]  Gerasimos Theotokatos,et al.  Numerical study of propulsion system performance during ship acceleration , 2018 .

[37]  Qiang Meng,et al.  Shipping log data based container ship fuel efficiency modeling , 2016 .

[38]  Hui Chen,et al.  Computational investigation of a large containership propulsion engine operation at slow steaming conditions , 2014 .

[39]  H. Kretzschmar,et al.  The IAPWS Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam , 2000 .

[40]  Lars Eriksson,et al.  Modeling and Control of Engines and Drivelines , 2014 .

[41]  Lokukaluge P. Perera,et al.  Marine Engine-Centered Data Analytics for Ship Performance Monitoring , 2017 .

[42]  Paola Gualeni,et al.  Monitoring and Analysis of the Performance Data of a RO-PAX Ship in the Perspective of Energy Efficiency , 2015 .

[43]  Zoran Lajic,et al.  Towards fault-tolerant decision support systems for ship operator guidance , 2012, Reliab. Eng. Syst. Saf..

[44]  H. K. Woud,et al.  Design of Propulsion and Electric Power Generation Systems , 2002 .

[45]  Hanna Barbara Rasmussen,et al.  Energy-efficient operational training in a ship bridge simulator , 2018 .

[46]  Cengiz Deniz,et al.  Environmental and economical assessment of alternative marine fuels , 2016 .

[47]  Gerasimos Theotokatos,et al.  A novel multi-objective decision support method for ship energy systems synthesis to enhance sustainability , 2018, Energy Conversion and Management.

[48]  Lars Eriksson,et al.  LiU CPgui: A Toolbox for Parameterizing Compressor Models , 2018 .

[49]  Pedro M. Saraiva,et al.  Combined Mechanistic and Empirical Modelling , 2004 .

[50]  R. Adland,et al.  The energy efficiency effects of periodic ship hull cleaning , 2018 .

[51]  F G Martins,et al.  The activity-based methodology to assess ship emissions - A review. , 2017, Environmental pollution.

[52]  A G Livanos,et al.  Simulation of large marine two-stroke diesel engine operation during fire in the scavenging air receiver , 2003 .

[53]  E. Fridell,et al.  Environmental assessment of marine fuels: liquefied natural gas, liquefied biogas, methanol and bio-methanol , 2014 .

[54]  Matthew A. Carlton,et al.  Data Analysis: Statistical and Computational Methods for Scientists and Engineers , 2020 .

[55]  R. Reitz Reciprocating Internal Combustion Engines , 2020, Fundamentals of Heat Engines.

[56]  Tom Dinham-Peren,et al.  Marine Propellers and Propulsion, 2ND edition , 2010 .

[57]  David Chalet,et al.  Exergy Analysis of Complex Ship Energy Systems , 2016, Entropy.

[58]  Francesco Melino,et al.  Optimal load allocation of complex ship power plants , 2016 .

[59]  T. J. Kotas,et al.  The Exergy Method of Thermal Plant Analysis , 2012 .

[60]  Tristan Smith,et al.  Barriers to energy efficient and low carbon shipping , 2015 .

[61]  Lokukaluge P. Perera,et al.  Data analysis on marine engine operating regions in relation to ship navigation , 2016 .