Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Neural network model for trim optimization of oil tankers in operation
Haiwen Tu2, 3, 4, Dawei You*, 1, 5, Yilong Ye*, 2, 3, Kai Xia2, 3
1 South China Sea Information Center of State Oceanic Administration, Guangzhou, China
2 College of Marine Science and Technology, China University of Geosciences, Wuhan, China
3 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
4 Shenzhen Research Institute, China University of Geosciences, Shenzhen, China
5 Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou, China

shipping companies to consider improving the energy efficiency ofships. As one of the ways to improve the energy efficiency of ships, theoptimum trim energy-saving technology is favored by shipping
companies and scholars in the field of shipbuilding because of its
advantages of not being affected by the types of ships and not needing
to change the structure. Scholars in the field of shipbuilding haveconducted a series of model simulations of ship optimum trim energysaving technology. Sherbaz et al. (2014) used the calculation methods
to optimize the trim of the KCS container ship, compared the calculated
values with the experimental values, found reasonable consistency
between the two kinds of values, and found the optimum trim withminimum resistance. Moustafa et al. (2015) used bulk carriers as theresearch object, and it is verified that the ship's resistance can be
reduced by optimizing the trim, thereby minimizing fuel use. Sun et al.
(2016) used CFD combined with model tests to carry out a study on thetrim optimization of 4250TEU container ships. And developed a trim
optimization system to guide the actual ship navigation. Lv et al. (2013,2018) applied the surface element method based on the potential flow
theory to calculate the wave-making resistance at different trim
conditions and found the optimum trim angle by using the trim
optimization response surface. The research described above indicatesthat the research on trim optimization is mainly carried out by
combining ship model tests and numerical calculations.
In the context of the rapid increase in computer performance, it
becomes feasible to process large amounts of data with neural networkmodels. The convenience and accuracy of neural network models have
led to their emergence in the fields of engineering and computing(Schmidhuber, 2015; Suk, 2017). Establishing ship prediction models
by neural network models and existing data has also become a newdevelopment direction. Hu et al. (2019) used backpropagation neuralnetwork (BPNN) and gaussian process regression (GPR) techniques toachieve prediction of ship fuel consumption data, the results showedthat both methods can accurately predict the fuel consumption of a ship
when it is affected by marine environmental factors. Yang et al. (2021)
conducted a study on resistance prediction of 13500TEU container
ships based on the radial basis function neural network, and the results
showed that the accuracy of ship resistance predicted by the neuralnetwork models is significantly better than that of the modified
admiralty coefficient (Tu et al., 2018). From the above, it is clear thatABSTRACT
In the context of carbon neutrality, the low-carbon development ofships has become the consensus of shipbuilding and shipping circles allover the world, and green ship technology will play an important role inthe development of low-carbon ships. As a kind of green technology forthe shipping industry, ship optimum trim energy-saving technology
does not need to modify the ship, but only needs to adjust the ship’s
bow and stern draft to reduce resistance. This report proposes a trim
optimization method for oil tankers based on the neural network model,
which can quickly obtain the optimum trim state of the oil tanker withthe minimum resistance during operation. Firstly, combined with thetrim optimization data of oil tanker model, backpropagation neuralnetwork, radial basis function neural network, GA-BP neural network,and generalized regression neural network were trained respectively,
and four prediction models for resistance of oil tanker model wereestablished. Then, by analyzing and comparing the performance of thevalidation set on the four neural network models, the model with thebest performance was selected as the trim optimization prediction
model. Finally, the eigenvalue data corresponding to the training data
set during the operation of the oil tanker was input into the trim
optimization prediction model to obtain the optimum trim with theminimum resistance of the oil tanker. The research on oil tanker trim
optimization based on neural network models is of great significance toenergy conservation and emission reduction of the shipbuilding
industry, and to promote the green development of ships.

KEY WORDS:Trim optimization technology; Neural networkmodel; Optimum trim.

INTRODUCTION
Oil plays an increasingly important role in world trade as its
increasingly widespread use as a source of energy (Ag et al., 2020;
Michail et al., 2020; Zhao et al., 2021), as one of the most importantways of oil transportation, the demand for oil tanker transportation is
also increasing rapidly (Lun et al., 2013). A large amount of polluting
gases emitted during the operation of ships and a share of energy
consumption in the operating costs (Jia et al., 2018) have forced

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