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

基本信息

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

项目摘要

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.

港口被视为空气污染物和温室气体（GHG）排放的集中区域。集装箱港口在全球经济中发挥着重要作用，按价值计算，它们处理着世界海运贸易的 50% 以上。由于贸易量激增、破坏性事件以及相关利益相关者之间缺乏协调，集装箱港口经常出现效率低下和严重拥堵的情况。港口拥堵在集装箱堆放或取回时需要额外和非生产性的移动，从而导致船只和卡车的周转时间更长。根据费利克斯托港发布的 2019-20 年环境报告，约 60% 的温室气体排放量（相当于港口作业产生的 3.43 万吨二氧化碳来自化石燃料堆场起重机和内部卡车。部署的卡车车队每年行驶超过 1,400 万公里，每年消耗约 420 万升柴油，每年产生 2.65 万吨二氧化碳。起重机车队每年消耗约 600 万升柴油，每年产生近 7800 吨二氧化碳。该港口承认，近 30% 的起重机运输是低效的，改进堆场管理、减少空载时间，可以大幅减少燃料消耗和温室气体排放（可能减少 15%，即 150 万升燃料和 6.1 万吨二氧化碳）。该项目应用机器学习和优化技术等数字技术来开发新的决策支持系统，以减少无效的起重机移动和卡车行驶距离。因此，产品生产率和效率将得到提高，可以在时间窗口内处理更多的集装箱，并且船舶和卡车的周转时间将减少。卡车、远洋船舶和货物装卸设备的温室气体排放量将减少。该项目将通过提高海运效率，直接使集装箱港口受益。该决策支持系统将作为物理和数字生态系统的一部分发挥作用，这将促进海上自治的发展并支持英国向“零排放”航运过渡。该项目还将通过流程自动化、降低成本、促进贸易量和经济增长，间接惠及包括航运公司、铁路运营商和托运人在内的其他利益相关者。我们的创新重点在于：（i）开创性尝试应用数字技术来预测进口集装箱卸船时的出港情况，以改善堆垛操作； (ii) 使用预测模型与规范模型相结合的突破性方法来支持堆场集装箱分配决策； (iii) 加深对决定因素（集装箱属性）相对重要性的认识，以预测集装箱的出港，并量化每个因素对预测的贡献。可量化的信息将为海事政策制定提供信息，例如引入适当的法规或激励计划，鼓励港口和利益相关者之间的信息共享，从而提高港口的运营效率并减少温室气体排放。