Variability and uncertainty analysis of wood waste as a feedstock for gasification

一个可靠的数学模型将被开发出来，并用于改进下吸式气化炉技术，将垃圾填埋场的木材废料转化为热能和电能。模型方程和相关的统计分析将使人们更加了解木材废料的内在可变性及其对气化炉性能的影响，气化炉生产用于发电的生物基燃料。该模型将用于为气化炉系统开发改进的操作和自动化策略，从而提高效率和原料灵活性。在加拿大的垃圾填埋场，目前有大量含有生物成分的剩余废物（例如，建筑和拆除废物和木托盘）。最近的研究表明，向下气流气化显示了利用这些基于木材的废物作为产生热量和电力的原料的希望。拟议的研究将建立在两个伙伴组织：加拿大自然资源部（NRCan）和加拿大国家研究委员会（NRC）最近进行的实验和模型研究的基础上。现有的NRC模型提供了气化炉行为的合理定性描述，但需要改进模型中使用的动力学和传输参数的估计，以便验证模型并获得可靠的定量预测。女王大学开发的统计方法将与NRC气化炉数据一起使用，以获得具有改进参数值的更新模型。改进后的模型将用于指导新的气化实验，这将有助于进一步改进模型。由此产生的模型将用于制定灵活的操作和控制策略，以减轻原料变化的影响，并确保可靠和经济的过程操作和气体燃料生产。

A reliable mathematical model will be developed and used to improve downdraft gasifier technology for converting landfill-diverted wood waste to heat and power. The model equations and associated statistical analysis will result in enhanced understanding of the inherent variability of wood waste and its influence on the performance of a gasifier, which produces bio-based fuel for electrical power generation. The model will be used to develop improved operating and automation strategies for the gasifier system, so that efficiency and feedstock flexibility can be improved. At Canadian landfill sites, there is currently a surplus of waste with a significant biogenic component (e.g., construction and demolition waste and wooden pallets). Recent studies show that downdraft gasification shows promise for utilization of these wood-based wastes as feedstock for generating heat and power. The proposed research will build on recent experimental and modeling research conducted by the two partner organizations: Natural Resources Canada (NRCan) and the National Research Council of Canada (NRC). An existing NRC model provides a reasonable qualitative description of gasifier behaviour, but improved estimates of kinetic and transport parameters used in the model are required so that the model can be validated and reliable quantitative predictions can be obtained. Statistical methods developed at Queen's University will be used with NRC gasifier data to obtain an updated model with improved parameter values. The improved model will then be used to guide new gasification experiments that will aid further model improvement. The resulting model will then be used to develop flexible operating and control strategies, which are needed to mitigate the effects of feedstock variability and to ensure reliable and economical process operation and gaseous fuel production.