The Analysis Of The Crude Oil's Heating Economy For Oil Tanker On The Basis Of BP Neural Network

With the fast development of our country's economy, the demand for oil increased. Several years ago, China has become the second oil consumption country in the world which only inferior to the USA. But the slow increase of domestic oil output couldn't meet the needs for oil consumption. So, the gap between oil supply and oil demand can only be closed by importation. Nowaday, Chinese tanker fleets are undertaking the mission of oil import shipping. The cost control should be carefully considered by each trade company. So, it's necessary to analyze the economy in the process of oil transportation. As we all known that, in the process of oil transportation, the high viscidity of crude oil can result in the difficulty to unload the oil. So, the cargo oil must be heated to some temperature in order to guarantee unload the cargo oil successfully when ship arrives the goal port.The BP Network Technology, based on MATLAB toolbox, was used in this paper to study the oil's optimal heating time. Firstly, the feasibility of Neural Network was analyzed. Secondly, the Neural Network model of oil heating time was established through studying, training and testing the samples. Finally, a rather satisfied result was obtained by solving the model. The method which adopted mathematic tools in this paper brought less error than the physical method which simplified the reality process greatly. So, in practice application, this method is much more simple and utility.By designing and taking a series of perpendicular experiments, the effect degree of factors that influenced the oil's heating time were found. Finally, primary factors and secondary factors were distinguished in this paper.