Multi-target Speed Optimization Of Ocean-going Ships Based On Digital-analog Drive

In the context of economic globalization,the prosperity of the shipping industry has also brought increasingly serious environmental pollution problems.The International Maritime Organization(IMO)has set greenhouse gas emission reduction targets and improved emission reduction laws and regulations for the shipping industry in multiple meetings.Economic benefits are the constant pursuit of shipping companies,and the sluggish shipping market has put dual pressure on shipping companies to achieve both environmental and economic benefits.Speed optimization is an effective means to improve the energy efficiency level of ship operation and reduce the cost of operating ships.It is an effective measure for shipping companies to control greenhouse gas emissions and improve operational efficiency.Taking an ocean-going VLCC as the research object,the main engine fuel consumption principle model was established according to the principle of ship propulsion,and the wind and wave drag increase data set was further solved and preprocessed by applying the real ship collection data,and the wind and wave drag increase machine learning model was constructed based on the extreme random tree method and Bayesian hyperparameter optimization method,and the main engine fuel consumption digital-analog driving model was jointly constructed with the principle model.The three-objective speed optimization model of ships is constructed by considering the carbon emissions,total operating costs and sailing time of ships,and the Pareto non-inferior solution set is solved by discrete solution method and genetic algorithm,and the best compromise solution is obtained based on the ideal point method,and the three-objective speed optimization research of ocean-going ships is carried out,and the results of two-target and single-target speed optimization are compared and analyzed.The results show that the error of effective power and propeller received power under static water conditions is within 3%,the trend of voluntary speed loss and involuntary speed loss is reasonable,and the trend of wind wave drag increase calculated by the principle model is correct.The relative error of the main engine fuel consumption digital-analog drive model is within 9%,and the average value is within 4.5%,and it has good prediction reliability.Under the market conditions of ships with high oil prices and low charter rates,compared with the single-objective optimization of sailing time,the three-objective optimization reduced carbon emissions by 417.2t and operating costs by more than US$60,000 at the cost of extending the sailing time by 7.1%,with a reduction rate of 17.5% and 10.0%,respectively,and shortened the sailing time by 18.5h at the cost of 7.7% and 3.2% increase in emissions and operating costs.The Pareto non-inferior solution set obtained by the genetic algorithm is more uniformly distributed,while the non-inferior solution set obtained by the discrete solution method has a wider distribution.The three-goal speed optimization achieves an effective balance between carbon emissions,operating costs and sailing time,and provides feasible technical support for ship energy saving,emission reduction and improvement of operating economy.