Development of predictive emission monitoring system (PEMS)

开发预测排放监测系统（PEMS）

• 批准号: 535813-2018
• 负责人: Du, Ke
• 金额: $ 1.46万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=698781
• 关键词: Development; predictive; emission; monitoring; system

Predictive Emission Monitoring Systems (PEMS) predict the air pollutant emissions from industrial sources based on the known operating parameters in combination with key process conditions (e.g., fuel consumption, ambient conditions, etc.). The objective of the proposed research is to monitor NOx, exhaust flow, temperature from combustion sources by applying the state-of-the-art machine learning technologies, particularly deep learning architecture, to environmental monitoring and aims to bring cost-effective environmental monitoring technologies to Canadian industries. The capital cost for PEMS is estimated to be 50% of a comparable Continuous Emission Monitoring System (CEMS); furthermore, the operations & maintenance (O&M) costs for PEMS are expected to be approximately 10% to 20% of the cost of CEMS. The developed product will be deployed in the sponsor's new facility for air monitoring in Alberta. It can also be included as an option to third-party licensees of the sponsor's Hydrofraction technology. Although the number of commercial PEMS installations in the US and Europe has significantly increased since 1990, PEMS implementation and utilization in Canada, particularly in Alberta, remains low. The major reasons and challenges include 1) Industry awareness 2) Software licensing, 3) lack of clarities of the regulatory approval process for PEMS installation and application, 4) lack of transferability from one facility to another, and 5) limited predicted parameters (variables). The research will (1) Develop next generation PEMS model using open source software for PEMS to reduce the software licensing cost; (2) Increase PEMS' transferability. The model can be used on a number of facilities directly, instead of facility-specific model; (3) Include all regulatory-required parameters as model outputs; therefore, CEMS unit can be completely replaced; (4) Obtain provincial (Alberta) and federal regulatory agencies' approval to pave a way for a broader installation of PEMS in Canadian industries.

预测性排放监测系统（PEMS）基于已知的操作参数结合关键工艺条件（例如，燃料消耗、环境条件等）。拟议研究的目标是通过将最先进的机器学习技术，特别是深度学习架构应用于环境监测来监测燃烧源的NOx，废气流量和温度，旨在为加拿大工业带来具有成本效益的环境监测技术。PEMS的资本成本估计为可比连续排放监测系统（CEMS）的50%;此外，PEMS的运营和维护（O&M）成本预计约为CEMS成本的10%至20%。开发的产品将部署在赞助商在阿尔伯塔的空气监测新设施。它也可以作为赞助商的加氢分馏技术的第三方许可证持有者的一个选项。 尽管自1990年以来，美国和欧洲的商业PEMS安装数量显著增加，但在加拿大，特别是在阿尔伯塔，PEMS的实施和利用率仍然很低。主要原因和挑战包括1）行业意识2）软件许可，3）缺乏PEMS安装和应用的监管批准过程的清晰度，4）缺乏从一个设施到另一个设施的可转移性，以及5）有限的预测参数（变量）。 本研究将（1）利用开放原始码软体开发下一代的PEMS模型，以降低软体授权成本;（2）增加PEMS的可移转性。该模型可以直接用于许多设施，而不是设施特定的模型;（3）包括所有法规要求的参数作为模型输出，因此，CEMS单元可以完全替换;（4）获得省级（阿尔伯塔）和联邦监管机构的批准，为在加拿大工业中更广泛地安装PEMS铺平道路。