A Grey-box model approach using noon report data for trim optimization

Robert H. Zwart, Jordi Bogaard, Austin A. Kana*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Trim optimization improves the energy efficiency of ships, thus reducing operational costs and emissions; however, trim tables are only available for a limited number of ships. There is thus a desire to develop additional, more accurate trim tables without the need for expensive model testing. The objective of this research was to develop a method to decrease fuel consumption by trim optimization, by a dynamic shaft power estimation model based on available operational data. A method that uses noon report data and a grey-box modelling approach is proposed. The grey box model consists of a multi-layer feedforward neural network to estimate the required shaft power, using operational parameters and an initial estimate of the required shaft power. A case study is presented for a modern chemical tanker and sea trials have been conducted to validate the results. The method provides correct trim advice for full load conditions; however, the magnitude of the effect is smaller compared to sea trial results. The model is able to estimate the required power with an average accuracy of over 6% for a random subset of the noon report data. Due to challenges inherent to noon reports as a data source, the actual effect of trim and speed have a bigger magnitude than the extracted trend.

Original languageEnglish
Pages (from-to)41-63
JournalInternational Shipbuilding Progress
Issue number1
Publication statusPublished - 2023


  • chemical tankers
  • grey-box modelling
  • noon reports
  • sea-trial validation
  • Trim optimization


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