船舶油耗模型研究

发布时间：2018-03-11 00:07

本文选题：船舶能效管理计划SEEMP　切入点：油耗模型　出处：《上海交通大学》2015年硕士论文　论文类型：学位论文

【摘要】：国际海事组织IMO拟通过船舶能效管理计划SEEMP建立适用于所有营运船舶的强制性能效标准,逐步控制和减少海上的CO2排放总量。而燃油开支在船舶运行成本中所占比例最大,该计划在降低排放的同时,必然会减少能耗并带来可观的燃油开支节约。上述经济、环境及法律法规因素共同决定了船舶必须以更低的油耗、更少的排放投入未来的实际运营。这在技术和管理两方面都对船舶和船队提出了更高的要求。船舶能效管理计划SEEMP的实施必须透彻理解与船舶油耗相关的各种影响因素,并据此建立船舶油耗的预测模型。本文正是基于上述背景,寻求建立油耗模型的通用方法,量化各种环境及操作因素对于油耗和航速的影响程度,为航行优化提供决策基础,对于提高船舶能效运行指数EEOI具有决定意义。本文将丹麦籍客滚轮MS Smyril号作为研究案例,对船舶实测运行数据进行了分析和预处理,并使用人工神经网络构建了船舶油耗和航速的黑箱模型。基于实测数据与模型预测数据的对比,验证了上述油耗模型的准确性和实用性,对于后续研究和应用具有重要借鉴价值。
[Abstract]:The International Maritime Organization (IMO) IMO plans to establish mandatory energy efficiency standards for all operating ships through the ship Energy efficiency Management Plan (SEEMP) to gradually control and reduce the total amount of CO2 emissions at sea. While reducing emissions, the plan will inevitably reduce energy consumption and result in significant fuel savings. These economic, environmental, and regulatory factors together determine that ships must have lower fuel consumption. Less emissions will be put into practical operation in the future. This puts higher demands on ships and fleets both technically and administratively. The implementation of the ship's energy efficiency management plan (SEEMP) must have a thorough understanding of the various factors that affect the ship's fuel consumption. Based on the above background, this paper seeks a general method to establish the fuel consumption model, quantifies the influence of various environment and operation factors on the oil consumption and speed, and provides the decision basis for the navigation optimization. In this paper, the Danish passenger roller MS Smyril number is taken as a case study to analyze and preprocess the actual ship operation data. The black-box model of ship fuel consumption and speed is constructed by using artificial neural network. The accuracy and practicability of the model are verified by comparing the measured data with the predicted data. It has important reference value for follow-up research and application.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U676.3

【参考文献】