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.

Keywords:

• Control charts
• hotelling control charts
• multivariate control charts
• process monitoring
• regression estimation
• statistical decision-making

Acknowledgments

The authors are extremely grateful to the Editor and all the Referees for their valuable suggestions and insight to significantly improve the article. The authors are also grateful to the Grimaldi Group’s Energy Saving Department engineers Dario Bocchetti and Andrea D’Ambra.

Additional information

Notes on contributors

Antonio Lepore

Antonio Lepore received the PhD degree in Aerospace, Naval and Quality Engineering from the University of Naples Federico II. He is currently a researcher at the Department of Industrial Engineering of the University of Naples Federico II, Italy. His main research topics include reliability, renewable energies, statistical process control, and assessment of risks from natural hazards.

Biagio Palumbo

Biagio Palumbo is Associate Professor in Statistic for Experimental and Technological Research at the Department of Industrial Engineering of the University of Naples Federico II. His major research interests include reliability, design and analysis of experiments, and statistical methods for process monitoring and optimization.

Christian Capezza

Christian Capezza holds an MSc degree in Engineering Management from the University of Naples Federico II and is a PhD student in Industrial Engineering at the same university. He is currently a visiting PhD student in Statistics at the Department of Statistical Sciences of the University of Padua. His research interests are centered in applied statistics and analysis of big data in engineering.