Research On Energy Consumption Based On Monitoring Data Of Marine Diesel Engine

With the development of global trade trends,ships,as the main means of transportation for global trade,have gradually increased their greenhouse gas emissions.The International Maritime Organization has established many restrictions on ship emissions to reduce ship emissions of greenhouse gases.At the same time,the shipping industry is constantly exploring environmental protection measures for ships to reduce the consumption of ship fuel.At present,the research on ship energy consumption has become a hot research topic.This paper aims to reduce the total fuel consumption of inland watercraft voyages,and researches the experimental ship in sections based on navigable environment and hydrological information,establishes a prediction model of main engine fuel consumption for regional voyages,and combines the neural network to convert the fuel consumption forecast into an objective function optimization problem.This problem is solved with the help of genetic algorithm tools to obtain the best main engine speed of the flight segment,and the main engine speed of each flight segment is given,so as to achieve the purpose of reducing the total fuel consumption of the ship voyage,energy saving and emission reduction.The specific research content is as follows:First of all,in order to obtain the data needed to study the energy consumption of the ship,the necessary collection equipment was installed on the experimental ship.After the installation of the acquisition instrument is completed,a data acquisition program is written based on the Raspberry Pi to enable stable and real-time data transmission.On this basis,a ship management platform is designed,which can view ship dynamics in real time,and can also perform simple analysis on the data collected by the ship.Secondly,obtain the relevant data collected by the experimental ship,use Python to preprocess and clean the data,and analyze the data to obtain valid data.Combining the characteristics of inland watercrafts,a fuel consumption prediction model for marine diesel engines is established.Using the upstream data during the flood period as the data set,the established fuel consumption model is trained and verified.The predicted value of the fuel consumption model training is 95.668%,and the verified predicted value differs from the actual value by 6%,indicating that the fuel consumption prediction model performs wellThen,on the basis of the marine diesel engine fuel consumption prediction model,a genetic algorithm is selected to optimize the input parameters of the marine fuel consumption model.The input parameters of the fuel consumption prediction model are analyzed,the engine speed is selected as the optimization object,and the output value of the fuel consumption prediction model is used as the fitness function of the genetic algorithm,and the optimization research is carried out on the input parameters of the engine speed.The experimental results show that searching for the best engine speed in the regional flight segment not only reduces the fuel consumption of the ship,but also reduces the emissions of pollutants from the ship.Finally,with the help of China Classification Society’s assessment of ship energy consumption related indexes,the energy consumption of ships in the regional segment was evaluated,and four different segment levels of excellent,good,medium and poor were obtained.The actual fuel consumption value of the ship’s entire voyage is compared with the actual total fuel consumption value of the regional segment,and the fuel consumption of the regional segment is reduced by 3.46% compared with the actual fuel consumption value,and the EEOI is also reduced by 3.57%,which is within the scope of practical engineering applications.