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

将开发一个可靠的数学模型，并用于改进下吸式气化炉技术，将垃圾填埋场转移的木材废物转化为热量和电力。模型方程和相关的统计分析将导致木材废物的固有的可变性及其对气化炉的性能，产生生物基燃料发电的影响，提高理解。该模型将用于开发气化炉系统的改进操作和自动化策略，从而提高效率和原料灵活性。在加拿大的垃圾填埋场，目前有大量的废物含有重要的生物成分（例如，建筑及拆卸废物及木托盘）。最近的研究表明，下吸式气化显示出利用这些木材为基础的废物作为原料产生热量和电力的承诺。拟议的研究将建立在最近的实验和建模研究进行的两个伙伴组织：加拿大自然资源（NRCan）和加拿大国家研究理事会（NRC）。现有的NRC模型提供了一个合理的定性描述气化炉的行为，但需要改进的动力学和运输参数的模型中使用的估计，使该模型可以验证和可靠的定量预测，可以得到。皇后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。然后，将使用所得到的模型来制定灵活的操作和控制策略，这是减轻原料可变性的影响和确保可靠和经济的工艺操作和气体燃料生产所需要的。

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.