Digitalisation for operational efficiency and GHG emission reduction at container ports

数字化可提高集装箱港口的运营效率并减少温室气体排放

• 批准号: EP/W028492/1
• 负责人: Dongping Song
• 金额: $ 5.43万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW028492%2F1
• 关键词: Digitalisation; operational; efficiency; GHG; emission

Ports are regarded as concentrated areas producing air pollutants and greenhouse gas (GHG) emissions. Container ports play an important role in the global economy as they handle over 50% of seaborne world trade by value. Due to surging trade volume, disruptive events, and lack of coordination across relevant stakeholders, container ports often experience inefficiency and severe congestion. Port congestion creates the requirements for extra and unproductive moves when containers are stacking or retrieving, resulting in longer turnaround times for vessels and trucks.According to the Environmental Report 2019-20 produced by the Port of Felixstowe, about 60% GHG emissions (equivalent to 34.3K tons of CO2) from port operations originate from fossil fuelled yard cranes and internal trucks. The deployed fleet of trucks travels more than 14 million km a year, consuming about 4.2 million litres of diesel fuel per year and producing 26.5K tons of CO2 per year. The fleet of cranes consumes around 6.0 million litres of diesel fuel per year and generates nearly 7.8K tons of CO2 yearly. The port acknowledges that nearly 30% crane movement is unproductive, and improvements in yard management, reducing the empty travel time, can dramatically reduce both fuel consumption and GHG emissions (potentially by 15%, i.e. 1.5 million litres of fuel and 6.1K tons of CO2). This project applies digital technologies such as machine learning and optimisation techniques to develop a new decision support system to reduce unproductive crane movement and truck travel distance. As a result, the product productivity and efficiency will be improved, more containers can be handled within time windows, and vessel and truck turnaround times will be reduced. GHG emissions from trucks, ocean-going vessels and cargo handling equipment will be reduced. The project will directly benefit container ports, by improving ocean freight efficiency. The decision support system will work as a part of a physical and digital ecosystem which will facilitate the development of maritime autonomy and support the UK's transition towards 'zero-emission' shipping. The project will also indirectly benefit other stakeholders including shipping lines, rail operators and shippers, by automating process, reducing their costs, boosting trading volume and economic growth. Our innovation focuses on: (i) the pioneering attempt to apply digital technologies to predict import containers' out-terminals at the point when they are discharged from vessels to improve stacking operations; (ii) using the ground-breaking approach of combining predictive models with prescriptive models to support yard container allocation decisions; (iii) advance the knowledge on the relative importance of determinant factors (container attributes) to predict containers' out-terminals and quantify the contributions made by each factor to the prediction. The quantifiable information will inform maritime policy making, for example, introducing appropriate regulations or incentive programs, to encourage information sharing between ports and the stakeholders, so as to improve operational efficiency and reduce GHG emissions at ports.

港口被视为产生空气污染物和温室气体（GHG）排放的集中区域。集装箱端口在全球经济中起着重要作用，因为它们按价值处理了超过50％的海洋世界贸易。由于贸易量增加，破坏性事件以及在相关利益相关者之间缺乏协调性，集装箱端口通常经历效率低下和严重的拥塞。当容器堆叠或检索集装箱时，港口的混合物对船只和卡车的周转时间较长。部署的卡车舰队每年行驶超过1400万公里，每年消耗约420万升柴油，每年生产26.5万吨二氧化碳。起重机的机队每年消耗约6000万升柴油，每年产生近7.8千吨的二氧化碳。该港口承认，近30％的起重机运动是无效的，院子管理的改善，减少了空荡荡的旅行时间，可以大大减少燃料消耗和温室气体的排放（可能增加15％，即150万升燃料和6.1 k吨CO2）。该项目应用了数字技术，例如机器学习和优化技术，以开发新的决策支持系统，以减少非生产性的起重机运动和卡车旅行距离。结果，将提高产品生产率和效率，可以在时间窗口内处理更多的容器，并且船舶和卡车周转时间将减少。卡车，远洋船只和货物处理设备的温室气体排放量将减少。该项目将通过提高海洋货运效率直接受益于容器端口。决策支持系统将作为物理和数字生态系统的一部分，该系统将有助于开发海上自主权，并支持英国向“零排放”运输的过渡。该项目还将通过自动化流程，降低成本，提高交易量和经济增长来间接使其他利益相关者（包括运输线，铁路运营商和托运人）受益。我们的创新重点是：（i）开创性的尝试应用数字技术来预测容器在船上的终端，以改善堆叠操作； （ii）使用将预测模型与规定模型相结合的开创性方法来支持院子集装箱分配的决策； （iii）促进有关决定因素（容器属性）相对重要性的知识，以预测容器的外末端，并量化每个因素对预测的贡献。可量化的信息将为海上政策制定提供信息，例如引入适当的法规或激励计划，以鼓励港口和利益相关者之间的信息共享，以提高运营效率并减少港口的温室气体排放。

项目成果

Text classification in shipping industry using unsupervised models and Transformer based supervised models

• DOI: 10.48550/arxiv.2212.12407
• 发表时间: 2022-12
• 期刊: ArXiv
• 作者: Yingyi Xie;Dongping Song

Predicting out-terminals of import containers at seaports through data analytics: incorporating unstructured data and measuring operational costs induced by misclassifications

通过数据分析预测海港进口集装箱的出港：纳入非结构化数据并衡量错误分类引起的运营成本

• 发表时间: 2023
• 作者: Xie Y