Smart Filtration Technologies: Optimising Flue Gas Filtration Assets in the UK Energy from Waste Sector

智能过滤技术：优化英国垃圾发电行业的烟气过滤资产

• 批准号: 2797605
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2797605
• 关键词: Smart; Filtration; Technologies; Optimising; Flue

Project backgroundCurrently, there is an expansion in the UK for the provision of power and heat from waste to energy and biomass combustion plant which assist the UK Government in its goal to reduce CO2 emissions while solving disposal problems. However, this type of plant generates particulate emissions, particularly PM2.5, which provides technological challenges in their control due to their size and health hazard potential. Increasingly stringent emissions control legislation means that existing technology will need to be improved while maintaining operational efficiency. This provides challenges to minimise outage caused by filter failures in baghouses where higher performance filter installation can highlight weaknesses in designs related to optimisation of flue gas flow patterns as well as identification of regions of high material stresses by simulation work.Project activityThe student will have the opportunity for significant placement with industry and access to facilities jointly developed by the University of Sheffield and Durham Filtration. The facility includes a pilot scale, fluidised bed, biomass/waste combustor and the successful student will join the team running tests on this facility. The student will also have access to new, state of the art filter testing equipment, closely linked to the sponsors activities.It is recognised that particulate control from biomass gasification processes presents different sets of challenges for plant operators and we will work with customers using this technology to offer improved systems as an outcome from the areas of research proposed below. The successful candidate will be expected to become involved in two or more of the topic areas with the exact project being decided after interview with students to match interests and capabilities.Topics1. Develop a predictive toolkit for filtration asset owners to give quantitative predicted time-to-failure or optimal regions of operation (economic or environmental optima). Analyse, interpret, and visualise a high volume of data from a filtration specific sensor platform installed in a UK biomass combustion and gasification plant. Integrate with existing plant data collection methods and provide a structured model for plant performance. Develop a reporting package to deliver data visualisations to the end user in a meaningful way, reporting on the benefit of any corrective action predicted by the model.2. To run experimental programmes on the new combustion test facility hosted by DF at their Jarrow facility. The project will investigate the generation of particulate material under pilot plant operating conditions from a range of fuel blends that are typical of energy from waste and biomass energy plant. The controlled conditions of the CTF will be used to focus on issues created by particular fuel blends which is not possible by the analysis of industrially harvested samples from full scale plant. The results will be complimentary and combined they will offer insight into design solutions that can be offered by DF to the industry.3. Develop new test methods and metrics for quantifying 'filter health' with meaningful interpretations for end users. Evolve the current ISO testing standards, based on old textile manufacture methods, to bring about a new, market-specific testing standard and industrial best practice. Using a wealth of industrial samples, build a library of data and assist with model-based predictions of filter performance.

项目背景目前，英国正在扩大从废物到能源和生物质燃烧工厂的电力和热量的供应，这有助于英国政府在解决处置问题的同时减少二氧化碳排放。然而，这种类型的工厂产生颗粒物排放，特别是PM2.5，由于其规模和潜在的健康危害，在控制方面带来了技术挑战。日益严格的排放控制立法意味着，在保持运行效率的同时，需要改进现有技术。这为尽量减少袋式仓库中过滤器故障造成的停机带来了挑战，在袋式仓库中，性能更高的过滤器安装可以突出与优化烟气流动模式相关的设计中的弱点，以及通过模拟工作识别高材料应力区域。项目活动学生将有机会在工业界进行重要的实习，并使用谢菲尔德大学和达勒姆过滤公司联合开发的设施。该设施包括一个中试规模、流化床、生物质/废物燃烧器，成功的学生将加入对该设施进行测试的团队。学生也将有机会获得新的，最先进的过滤器测试设备，与赞助商的活动密切相关。人们认识到，生物质气化过程中的颗粒控制对工厂运营商提出了不同的挑战，我们将与客户合作，利用这项技术，提供改进的系统，作为下面提出的研究领域的成果。成功的候选人将参与两个或两个以上的主题领域，具体的项目将在与学生面试后决定，以匹配他们的兴趣和能力。为过滤资产所有者开发预测工具包，以定量预测故障时间或最佳操作区域（经济或环境最优）。分析、解释和可视化来自安装在英国生物质燃烧和气化工厂的过滤特定传感器平台的大量数据。与现有工厂数据收集方法集成，为工厂性能提供结构化模型。开发一个报告包，以有意义的方式向最终用户提供数据可视化，报告模型预测的任何纠正措施的好处。在DF在其贾罗工厂的新燃烧测试设备上运行实验项目。该项目将调查在试点工厂操作条件下，从一系列典型的废物能源和生物质能工厂的燃料混合物中产生颗粒材料。CTF的受控条件将用于关注由特定燃料混合物产生的问题，这是不可能通过分析从全尺寸工厂收获的工业样品来实现的。结果将是互补的，结合起来，他们将为DF为行业提供的设计解决方案提供见解。开发新的测试方法和指标，用于量化“过滤器运行状况”，并为最终用户提供有意义的解释。在旧的纺织制造方法的基础上，发展当前的ISO测试标准，以实现新的，特定于市场的测试标准和行业最佳实践。使用丰富的工业样本，建立一个数据库，并协助基于模型的过滤器性能预测。