Intelligent instrumentation for assessment and monitoring of hydrogen blend fuels in domestic boilers

用于评估和监测家用锅炉氢混合燃料的智能仪表

• 批准号: EP/X020789/1
• 负责人: Md Moinul Hossain
• 金额: $ 40.76万
• 依托单位: University of Kent
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX020789%2F1
• 关键词: Intelligent; instrumentation; assessment; monitoring; hydrogen

Significant reduction of greenhouse gas emissions (GHG) has become the utmost endeavour to achieve net-zero emissions by 2050. In the UK, domestic heating itself is responsible for 17% of the total GHG emissions, this is comparable to the contribution of all petrol and diesel cars (BEIS, January 2022). Therefore, the decarbonization of domestic heat is a big challenge. A sustainable route to reduce GHG is to replace natural gas (NG) with hydrogen (H2) since the combustion of H2 does not produce CO2. However, the challenge for H2 combustion is that its combustion characteristics substantially differ from NG (methane, CH4), e.g., its use affects combustion stability, heat release and NOx emission, and increases the combustion rate due to a higher H2 flame temperature. Various technological challenges are also associated with using pure H2 such as its production, safety, quick charge capability and low density, which limits its storage capabilities. At this transitional stage, a practical option is the use of higher H2 enriched fuel (i.e., more than 20% blend with NG), which would be a promising solution to lower the CO2 emission compared with other fossil fuels. However, the impacts of higher H2 enriched fuels on the widely used condensing heating boilers are not extensively studied and fully understood. The H2 enrichment leads to higher flame radicals such as OH*, CN*, CH* and C2*, higher combustion temperature and flame destabilisation, thus triggering higher NOx formation. The flame radicals are closely related to the combustion structure, temperature, heat release and pollution emissions. Moreover, domestic condensing boilers use premixed cylindrical/surface burners, and these burners produce an array of flames. It is extremely difficult to measure flame radical information in different depths of the array of flames using existing measurement systems. The development of an intelligent instrumentation system has, therefore, become indispensable to assess and monitor the flame radical emissions and NOx formation process at different depths of flames, thus facilitating an in-depth understanding of the combustion process of different H2/CH4 blends.This project will develop and implement a new instrumentation system based on multi-spectral light field imaging to assess and monitor the flame radicals and temperatures with different H2/CH4 blends in domestic boilers. Light field image formation and depth reconstruction models will be developed to generate flame radical images at different depths for different spectral bands. The developed system will provide distinctive capabilities for characterising and quantifying the radical information and temperature profiles of a flame in a single exposure, simultaneously. The proposed project will also develop an intelligent data-driven model based on machine learning to predict NOx emission, thus, facilitating the improvement of domestic boiler performance. The relationships between flame radical characteristics and NOx emission will be established by conducting a series of experiments initially on a lab-scale test rig and then on commercial domestic boilers under different H2/CH4 blends and boiler settings. The prototype system will also be tested on a gas turbine test rig to evaluate its wider applicability. Experiments will be conducted to investigate the characteristics of CO2, H2 and ammonia (NH3) blend combustion, thus providing an in-depth understanding of stability regions and NOx emission with different proportions of CO2/H2/NH3 in the blend.The outcomes of this research will provide in-depth knowledge of the combustion characteristics of H2 blends, understanding of the boiler efficiency and pollutant formation process of domestic boilers. Once the system is developed, it will be used for the design of domestic boilers, and the engineering insights produced during the project could be used to develop a portable diagnostic tool for routine monitoring of blended-fuel boilers.

大幅减少温室气体排放（GHG）已成为到2050年实现净零排放的最大努力。在英国，家庭供暖本身就占温室气体排放总量的17%，这与所有汽油的贡献相当和柴油车（BEIS，2022 年 1 月）。因此，生活热力脱碳是一个很大的挑战。减少温室气体的可持续途径是用氢气 (H2) 替代天然气 (NG)，因为氢气燃烧不会产生二氧化碳。然而，H2燃烧面临的挑战是其燃烧特性与NG（甲烷，CH4）有很大不同，例如，它的使用会影响燃烧稳定性、放热和NOx排放，并且由于H2火焰温度较高而增加燃烧速率。使用纯氢气还面临各种技术挑战，例如其生产、安全性、快速充电能力和低密度，这限制了其存储能力。在这个过渡阶段，一个实用的选择是使用更高浓度的富氢燃料（即与天然气混合超过 20%），与其他化石燃料相比，这将是降低二氧化碳排放的一个有​​前途的解决方案。然而，高浓度 H2 富集燃料对广泛使用的冷凝式供暖锅炉的影响尚未得到广泛研究和充分了解。 H2 富集会导致更高的火焰自由基（例如 OH*、CN*、CH* 和 C2*）、更高的燃烧温度和火焰不稳定，从而引发更高的 NOx 形成。火焰自由基与燃烧结构、温度、放热和污染排放密切相关。此外，家用冷凝锅炉使用预混圆柱形/表面燃烧器，这些燃烧器产生一系列火焰。使用现有的测量系统来测量火焰阵列的不同深度中的火焰自由基信息是极其困难的。因此，开发智能仪表系统来评估和监测不同火焰深度的火焰自由基排放和NOx形成过程，从而有助于深入了解不同H2/CH4混合物的燃烧过程。该项目将开发和实施一种基于多光谱光场成像的新型仪器系统，以评估和监测家用锅炉中不同 H2/CH4 混合物的火焰自由基和温度。将开发光场图像形成和深度重建模型，以生成不同光谱带的不同深度的火焰自由基图像。所开发的系统将提供独特的功能，可同时表征和量化单次曝光中火焰的基本信息和温度分布。该项目还将开发基于机器学习的智能数据驱动模型来预测氮氧化物排放，从而促进家用锅炉性能的提高。火焰自由基特性和氮氧化物排放之间的关系将通过首先在实验室规模的试验台上进行一系列实验，然后在不同的 H2/CH4 混合物和锅炉设置下的商业家用锅炉上进行。该原型系统还将在燃气轮机试验台上进行测试，以评估其更广泛的适用性。通过实验研究CO2、H2和氨(NH3)混合燃烧的特性，从而深入了解混合气体中不同比例的CO2/H2/NH3的稳定区域和NOx排放。研究将深入了解氢气混合物的燃烧特性，了解家用锅炉的锅炉效率和污染物形成过程。该系统开发完成后，将用于家用锅炉的设计，项目期间产生的工程见解可用于开发便携式诊断工具，用于混合燃料锅炉的日常监测。