Enhanced performance monitoring of in situ thermal treatment

清理受污染场地对加拿大具有经济、社会和环境效益。 许多场地（棕地）受到石油燃料、氯化溶剂、杂酚油和煤焦油等危险液体的污染。 可通过应用原地补救技术加快清理这些地点的土壤和地下水。 原位热处理技术是一组积极的补救技术，可用于各种复杂的场地，通过将土壤和地下水加热到水沸腾的温度并提取蒸汽和蒸发的污染物，在短时间内实现清理目标。 然而，由于高温和运行中断的高成本，这些技术的性能在运行时难以测量。 本研究的目标是开发新的监测技术和数学设计工具，以改善这些技术的监测和优化。 在现场规模的热修复应用过程中收集的数据将用于建立污染物去除和易于测量的参数之间的关系，例如单个提取威尔斯的地下温度和气体流速。 实验室实验将被用来调查在加热过程中的多组分污染物混合物的去除，以评估如何去除它们的化学签名可能不同于那些在去除单组分污染物。 将开发数学模型来预测污染物的去除，并将其与现场和实验室测量结果进行比较。 这项研究将由大学研究人员和行业合作伙伴组成的团队指导，并将培养两名硕士生。

The clean-up of contaminated sites is economically, socially and environmentally beneficial for Canada. Many sites (brownfields) have been contaminated by hazardous liquids such as petroleum fuels, chlorinated solvents, creosote and coal tar. The clean-up of soil and groundwater at these sites can be accelerated through the application of in situ remediation technologies. In situ thermal treatment technologies are a group of aggressive remediation technologies that can be used at a variety of complex sites to meet clean-up goals over short time frames by heating soil and groundwater to water-boiling temperature and extracting the steam and vapourized contaminants. However, the performance of these technologies is difficult to measure while they are in operation due to the high temperature and high cost of operational interruption. The goal of this research is to develop new monitoring techniques and mathematical design tools to improve the monitoring and optimization of these technologies. Data collected during a field-scale thermal remediation application will be used to develop relationships between contaminant removal and readily-measured parameters such as subsurface temperature and gas flow rate at individual extraction wells. Laboratory experiments will be used to investigate the removal of multi-component contaminant mixtures during heating, to assess how chemical signatures of their removal may differ from those generated during the removal of single-component contaminants. Mathematical models will be developed to predict contaminant removal, and will be compared to field and laboratory measurements. This research will be guided by a team of university researchers and industry partners, and will train two Master's students.