首页|Findings from Harbin Engineering University Reveals New Findings on Machine Learning (Joint Optimization of Ship Speed and Trim Based On Machine Learning Method Under Consideration of Load)
Findings from Harbin Engineering University Reveals New Findings on Machine Learning (Joint Optimization of Ship Speed and Trim Based On Machine Learning Method Under Consideration of Load)
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NETL
NSTL
Elsevier
New research on Machine Learning is the subject of a report. According to news reporting out of Harbin, People’s Republic of China, by NewsRx editors, research stated, “The maritime sector has diligently endeavored to mitigate fuel consumption to curtail emissions and expendi-tures within the sustainable development framework. To comprehensively analyze ships’ fuel consumption, considering the combined influence of multiple factors, we develop an integrated optimization approach encompassing speed, trim, and speed-trim adjustments under different loading conditions.” Financial supporters for this research include Harbin Engineering University, Ministry of Industry and Information Technology of the People’s Republic of China. Our news journalists obtained a quote from the research from Harbin Engineering University, “Firstly, we employ the fuel consumption prediction models established before, conduct a detailed analysis of the ship’s route in distinct segments, and formulate optimization models by scrutinizing the ship’s actual voyage. Secondly, we conducted single-parameter optimization for speed and trim to achieve the minimum fuel consumption for the entire route. We also implemented a joint optimization approach for simultaneously optimizing speed and trim to enhance fuel efficiency further. Lastly, we applied a smoothing method to the model’s prediction results to solve the step problem; and compared the optimization results before and after smoothing to assess the approach’s effectiveness. The results show that the joint optimization in the ballast condition yielded fuel consumption savings of 12.30% and 11.70% before and after smoothing, respectively; The fuel savings achieved under full load conditions were 10.18% and 9.47%.”
HarbinPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningHarbin Engineering University
NETL
NSTL
Elsevier
