Orthogonal LS-PLS approach to ship fuel-speed curves for supporting decisions based on operational data

Abstract The shipping industry relies on ship fuel-speed curves to describe the fuel consumption (and CO2 emissions levels) per hour as a function only of the vessel’s speed over ground, based on dedicated test data. However, they are affected by additional factors in real cases. In this article, a novel method is developed elaborating the orthogonal least-squares partial least-squares (LS-PLS) approach to enhance fuel-speed curves accuracy when information is available on additional factors from multi-sensor systems. Through real data examples, the approach is shown capable of detecting anomalies in CO2 emission levels and testing the effectiveness of ship energy efficiency initiatives.

[1]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[2]  Kevin A. Clarke.  The Phantom Menace: Omitted Variable Bias in Econometric Research , 2005 .

[3]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[4]  B. Efron,et al.  A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .

[5]  Biagio Palumbo,et al.  A Statistical Approach to Ship Fuel Consumption Monitoring , 2015 .

[6]  Szu Hui Ng,et al.  Uncertainty quantification of CO2 emission reduction for maritime shipping , 2016 .

[7]  Evangelos Boulougouris,et al.  Voyage optimisation: prediction of ship specific fuel consumption for energy efficient shipping , 2013 .

[8]  J. Corbett,et al.  Updated emissions from ocean shipping , 2003 .

[9]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[10]  J. Brian Gray,et al.  Introduction to Linear Regression Analysis , 2002, Technometrics.

[11]  A. Phatak,et al.  The geometry of partial least squares , 1997 .

[12]  David Ronen,et al.  The Effect of Oil Price on the Optimal Speed of Ships , 1982 .

[13]  Robert L. Mason,et al.  Biased Estimation in Regression: An Evaluation Using Mean Squared Error , 1977 .

[14]  Biagio Palumbo,et al.  A Procedure for Predicting and Controlling the Ship Fuel Consumption: Its Implementation and Test , 2015, Qual. Reliab. Eng. Int..

[15]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

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

[17]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[18]  Inge Norstad,et al.  Reducing fuel emissions by optimizing speed on shipping routes , 2010, J. Oper. Res. Soc..

[19]  Dimitris Konovessis,et al.  On the estimation of ship's fuel consumption and speed curve: A statistical approach , 2016 .

[20]  I. Helland.  ON THE STRUCTURE OF PARTIAL LEAST SQUARES REGRESSION , 1988 .

[21]  Basic Principles of Ship Propulsion , 2011 .

[22]  Henk Folmer,et al.  The choice of policy instruments for the control of carbon dioxide emissions , 1995 .

[23]  Gordon K. Smyth,et al.  Predicting Ship Fuel Consumption: Update , 1996 .

[24]  John F. MacGregor,et al.  Multi-way partial least squares in monitoring batch processes , 1995 .

[25]  David A. Schrady,et al.  Predicting ship fuel consumption: Update. Technical report , 1996 .

[26]  Tormod Næs,et al.  Combining designed experiments with several blocks of spectroscopic data , 2007 .

[27]  John F. MacGregor,et al.  Establishing Multivariate Specification Regions for Incoming Materials , 2004 .

[28]  C. Mar Molinero,et al.  Budgeting fuel consumption in a cruise liner , 1984 .

[29]  Osman Turan,et al.  An artificial neural network based decision support system for energy efficient ship operations , 2016, Comput. Oper. Res..

[30]  Z Bazari.  Ship energy performance benchmarking/rating: methodology and application , 2007 .

[31]  Nola D. Tracy,et al.  Multivariate Control Charts for Individual Observations , 1992 .

[32]  Atilla Incecik,et al.  A semi-empirical ship operational performance prediction model for voyage optimization towards energy efficient shipping , 2015 .

[33]  Theodora Kourti,et al.  Multivariate SPC Methods for Process and Product Monitoring , 1996 .

[34]  Nicole Krämer,et al.  An overview on the shrinkage properties of partial least squares regression , 2007, Comput. Stat..

[35]  T. Næs,et al.  A comparison of methods for analysing regression models with both spectral and designed variables , 2004 .