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- Title
A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants.
- Authors
Zhou, Shangli; He, Hengjing; Zhang, Leping; Zhao, Wei; Wang, Fei
- Abstract
Reducing CO 2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO 2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO 2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO 2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation Network (EEN), a heterogeneous network-based deep learning model, to estimate CO 2 emissions from CFPPs in near-real-time. We use artificial data generated by the simulator to train it and apply a few real-world datasets to complete the adaptation. The experimental results show that our proposal is a competitive approach that not only has accurate measurements but is also easy to implement.
- Subjects
CARBON emissions; COAL-fired power plants; CONTINUOUS emission monitoring; DEEP learning; MANUFACTURING processes; MILLENNIALS
- Publication
Energies (19961073), 2023, Vol 16, Issue 4, p1646
- ISSN
1996-1073
- Publication type
Article
- DOI
10.3390/en16041646