Comparative analysis on the fuel consumption prediction model for bulk carriers from ship launching to current states based on sea trial data and machine learning technique

MARPOL 73/78, Chapter IV is considered to reveal the emission control engineering on ships. The probability model of the fuel oil consumption is established based on the machine learning technique. The proposed methods are applied into this research in order to establish the probability model of fuel oil consumption. The combination of Monte Carlo (MC) simulation method with Artificial Neural Networks (ANNs) is an optimal solution to deal the fuel consumption of marine main diesel engine. The sample data has been established based on the Monte Carlo simulation method. The model of fuel oil consumption is designed by Artificial Neural Network method. The proposed prediction model of fuel oil consumption is based on a back-propagation training algorithm of ANNs method. The research results of proposed model have been verified with the actual operation data that have been collected from a certain bulk carrier of VINIC shipping transportation company in Vietnam. The collected data is the actual operation parameters from the noon-log report of voyage during two years of the ship. The probability model of fuel oil consumption for main diesel engine is very useful in the field of ships energy efficiency management with higher accurancy than the other previous models. (c) 2021 Shanghai Jiaotong University. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )