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

木材废料作为气化原料的变异性和不确定性分析

• 批准号: 550060-2020
• 负责人: McAuley, Kimberley
• 金额: $ 1.46万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721219
• 关键词: Variability; uncertainty; analysis; wood; waste

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.

将开发并使用可靠的数学模型来改进下吸式气化器技术，将垃圾填埋场转移的木材废料转化为热能和电力。模型方程和相关的统计分析将加深对木材废料固有变异性及其对气化炉性能影响的理解，气化炉生产用于发电的生物基燃料。该模型将用于开发气化器系统改进的操作和自动化策略，从而提高效率和原料灵活性。在加拿大垃圾填埋场，目前存在大量含有大量生物成分的废物（例如建筑和拆除废物以及木托盘）。最近的研究表明，下吸式气化有望利用这些木质废物作为产生热量和电力的原料。拟议的研究将建立在两个合作组织最近进行的实验和建模研究的基础上：加拿大自然资源部（NRCan）和加拿大国家研究委员会（NRC）。现有的 NRC 模型提供了气化器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，以便可以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 气化炉数据结合使用，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，这将有助于模型的进一步改进。然后，所得模型将用于开发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且经济的工艺操作和气体燃料生产。