Optimisation of hydrogen fuelling station operation and maintenance to maximise performance and resilience of key infrastructure

该项目与ITM Power合作，ITM Power在设计和部署基于电解槽的氢气解决方案方面处于领先地位，包括加氢站（HRS）。部署早期上市的工厂不仅可以优化现有工厂的运营，还可以改进下一代加油站的设计。显然，最大限度地提高HRS的性能不仅是出于经济原因，也是为了提供最佳的客户体验。该项目旨在优化工厂运营，计划预防性维护可以帮助减少服务中断并改善工厂的商业案例。有两种方法正在为该项目提出：1。基于数据的资产管理对单个“组件”（如电解槽、制冷系统、压缩机等）的调查，分析其操作、维护和故障数据。通过查看“组件”的性能，我们可以确定故障发生之前的时间，然后推荐预防性维护计划。操作参数（例如流速、温度、压力、功率需求等）在不久的将来，当部件不再处于正常操作参数范围内时，可以提供故障事件的早期指示，并识别对计划外预防性维护的任何要求。该项目将使用模式识别和机器学习方法分析现有工厂数据，以寻找故障的早期迹象。2.基于系统的资产管理采取更广泛的系统范围或网络范围的方法有助于提高弹性。在系统中，我们需要了解组件之间的冗余，因为一个组件故障可以通过冗余组件来补偿。在没有冗余或冗余有限的情况下，将进行成本效益分析，以指导未来弹性HRS的设计原则。在网络中，需要了解站点的位置，以及如果其中一个站点出现故障，对可靠传输的影响。这种分析将有助于指导HRS的战略集群，定义密度和优化的地理位置。要对系统或网络进行建模，基于Petri网的方法是更传统的故障树分析方法的合适替代方法。它将被用来模拟系统的性能，并评估其在故障发生时的恢复能力。所采取的方法（或上述方法的混合）将根据成功候选人的专业知识和兴趣进行调整。该项目将涉及在ITM Power工作的时间，无论是在谢菲尔德的新工厂还是在他们的HRS之一，以促进大学研究小组和ITM Power专家之间的有效知识转移。

This project is in collaboration with ITM Power , who are leading the sector in the design and deployment of electrolyser based hydrogen solutions, including hydrogen refuelling stations (HRS). The deployment of early-to-the-market plants provides the opportunity to optimise not only the operation of the existing plants but to also improve the design of next generation fuelling stations. It is obviously desirable to maximise performance of the HRS not just for economic reasons, but in order to deliver the best customer experience. This project seeks to optimise the plant operation, where planning preventative maintenance can help reduce disruption to service and improve the commercial case of a plant. There are two approaches being proposed for the project:1. Component-based asset managementInvestigation of the individual "components" (such as electrolyser, refrigerator system, compressor, etc.), analysing their operational, maintenance and fault data. Looking at the performance of a "component", we can identify how long it will be before a failure occurs and then recommend a preventative maintenance schedule. Operational parameters (e.g. flow rates, temperature, pressure, power demand, etc.) can provide early indications of a failure event in the near future when the component is no longer within normal operational parameters and identify any requirement for unscheduled preventative maintenance. The project will analyse existing plant data using pattern recognition and machine learning methods to look for early signs of failure. 2. System-based asset managementTaking a more system-wide or network-wide approach can help improve resilience. In a system we need to understand redundancies between components, as one component failure can be compensated by a redundant component. Where there is no or limited redundancy, a cost benefit analysis will be undertaken to guide future design principles for resilient HRS. In a network, there is a need to understand where the stations are located and what are the effects to reliable delivery if one fails. This analysis will help guide strategic clustering of HRS defining the density and optimised geographic location.To model a system or a network, a suitable alternative to a more traditional Fault Tree Analysis method would be a Petri net-based approach. It would be used to simulate system performance and evaluate its resilience when failures occur.The approach taken (or a blend from the above) will be tailored to the successful candidate's expertise and interests. The project will involve periods of working at ITM Power, either at their new factory in Sheffield or at one of their HRS, to facilitate effective knowledge transfer between the University research group and the experts at ITM Power.