Quantitative Characterisation of Flame Radical Emissions for Combustion Optimisation through Spectroscopic Imaging

通过光谱成像定量表征燃烧优化的火焰自由基发射

• 批准号: EP/G002398/1
• 负责人: Gang Lu
• 金额: $ 26.12万
• 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: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG002398%2F1
• 关键词: Quantitative; Characterisation; Flame; Radical; Emissions

The power generation industry relies heavily on coal despite the availability of other energy sources. The use of low quality coals, and coal blends from a variety of sources is becoming widespread in power plant for economic and availability reasons. Co-firing coal with biomass on existing coal fired furnaces is recognised as one of the new technologies for reducing CO2 emissions in the UK and the rest of the world. The changes in these fuel supplies have posed significant technical challenges for combustion plant operators and engineers to maintain high combustion efficiency and low atmospheric emissions including CO2, NOx, SOx and particulates. Despite various advances in developing the coal combustion and co-firing technologies, a range of technological issues remain to be resolved due to the inherent differences in the physical and combustion properties between coal and biomass. A typical problem associated with the use of low quality coal and co-firing of coal and biomass is the uncertainty in the combustion characteristics of the fuels, often resulting in poor flame stability, low thermal efficiency, high pollutant emissions, and other operational problems. To meet the stringent standards on energy saving and pollutant emissions, advanced technology for improved understanding of energy conversion, pollutant formation processes and consequent combustion optimisation in coal-biomass fired furnaces have therefore become indispensable.A flame, as the primary zone of the highly exothermic reactions of burning fuels, contains important information relating closely to the quality of the combustion process. Recent study has shown that the combustion process, particularly the pollutant emission formation processes, can be better understood and consequently optimised by monitoring and quantifying radical emissions within the flame zone through spectroscopic imaging and image processing techniques. It is proposed to develop a methodolgy for the monitoring and quantification of the radiative characteristics of free radicals (e.g. OH*, CH*, CN* and C2) within a coal-biomass flame and consquently the estimation of the emission levels in flue gas (e.g. NOx, CO2 and unburnt carbon). A vision-based instrumentation system, capable of detecting the radiative characteristics of the multiple radicals simultaneously and two-dimensionally, will be constructed. Computing algorithms will be developed to analyse the images and quantify the radiative characteristics of the radicals based on advanced signal processing techniques including wavelet analysis. The relationships between the characteristics of the radicals and fuel type and air supplies will be established. The emission levels in flue gas will be estimated based on characteristic features of the flame radicals obtained by the system. All data processing will be performed in an industrial computer system associating with integrated system software including a graphic user-interface. The system developed will be initially tested on a gas-fired combustion rig in University of Kent and then an industrial-scale coal combustion test facility run by RWE npower. A range of combustion conditions will be created during the industrial tests, including different coal-biomass blends and different fuel/air flowrates. The relationships between the emission characteristics of radicals and the chemical/physical properties of the fuels and the pollutant emissions will then examined under realistic industrial conditions.The outcome of this research will provide a foundation for a new area within coal-biomass combustion optimisation in which advanced flame monitoring techniques could help to predict emissions directly from the flame information instead of the flue gas measurement, shortening the control loop for emissions reduction. Such techniques would greatly benefit the power industry by allowing them burning fuels more efficiently and meanwhile reducing harmful emissions to the environment.

发电行业严重依赖煤炭，尽管有其他能源。由于经济和可用性的原因，低质量煤和来自各种来源的煤混合物的使用在发电厂中变得越来越普遍。在现有的燃煤炉上将煤与生物质共燃被认为是英国和世界其他地区减少二氧化碳排放的新技术之一。这些燃料供应的变化对燃烧设备操作员和工程师提出了重大的技术挑战，以保持高燃烧效率和低大气排放，包括CO2，NOx，SOx和颗粒物。尽管在发展煤燃烧和混烧技术方面取得了各种进展，但由于煤和生物质之间物理和燃烧性质的固有差异，一系列技术问题仍有待解决。与低质量煤的使用以及煤和生物质的共烧相关的典型问题是燃料的燃烧特性的不确定性，通常导致差的火焰稳定性、低的热效率、高的污染物排放和其它操作问题。为了满足节能减排的严格要求，研究煤-生物质混合燃料燃烧过程中的能量转化、污染物生成过程以及燃烧优化技术已成为不可或缺的重要内容。火焰作为燃料燃烧过程中高放热反应的主要区域，包含着与燃烧过程质量密切相关的重要信息。最近的研究表明，燃烧过程，特别是污染物排放的形成过程，可以更好地理解和优化，通过监测和量化自由基排放的火焰区域内，通过光谱成像和图像处理技术。提出了一种监测和量化煤-生物质火焰中自由基（如OH*、CH*、CN* 和C2）辐射特性的方法，并据此估算烟气中的排放水平（如NOx、CO2和未燃碳）。一个基于视觉的仪器系统，能够同时检测多个自由基的辐射特性和二维，将被构建。将开发计算算法，以分析图像，并根据包括小波分析在内的先进信号处理技术量化自由基的辐射特性。将建立自由基的特性与燃料类型和空气供应之间的关系。烟气中的排放水平将根据该系统获得的火焰自由基的特征来估计。所有数据处理将在与集成系统软件（包括图形用户界面）相关的工业计算机系统中进行。开发的系统将首先在肯特大学的燃气燃烧装置上进行测试，然后在RWE npower公司的工业规模燃煤测试设施上进行测试。在工业测试期间将创造一系列燃烧条件，包括不同的煤-生物质混合物和不同的燃料/空气流速。研究结果将为煤-生物质燃烧优化的新领域奠定基础，在该领域中，先进的火焰监测技术可以直接从火焰信息而不是烟气测量来预测排放，缩短了减排的控制回路。这些技术将使电力工业受益匪浅，因为它们可以更有效地燃烧燃料，同时减少对环境的有害排放。

项目成果

Imaging and characterisation of flame radical light emission for combustion optimisation

用于燃烧优化的火焰自由基光发射成像和表征

• 发表时间: 2010
• 期刊: Proceedings of the 8th European Conference on Coal Research and its Applications
• 作者: Krabicka J

Prediction of pollutant emissions of biomass flames using digital imaging, contourlet transform and Radial Basis Function network techniques

利用数字成像、轮廓波变换和径向基函数网络技术预测生物质火焰污染物排放

• DOI: 10.1109/i2mtc.2014.6860832
• 发表时间: 2014
• 作者: Li N

Prediction of Pollutant Emissions of Biomass Flames Through Digital Imaging, Contourlet Transform, and Support Vector Regression Modeling

• DOI: 10.1109/tim.2015.2411999
• 发表时间: 2015-03
• 期刊: IEEE Transactions on Instrumentation and Measurement
• 影响因子: 5.6
• 作者: Nan Li;G. Lu;Xinli Li;Yong Yan

Prediction of NOx emissions throughflame radical imaging and neural network based soft computing

• DOI: 10.1109/ist.2012.6295594
• 发表时间: 2012-07
• 期刊: 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings
• 作者: Xinli Li;Duo Sun;G. Lu;J. Krabicka;Yong Yan

On-line identification of biomass fuels based on flame radical imaging and application of radical basis function neural network techniques

• DOI: 10.1049/iet-rpg.2013.0392
• 发表时间: 2015-05-01
• 期刊: IET RENEWABLE POWER GENERATION
• 影响因子: 2.6
• 作者: Li, Xinli;Wu, Mengjiao;Liu, Shi
• 通讯作者: Liu, Shi