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A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants.
- Source :
- Energies (19961073); Feb2023, Vol. 16 Issue 4, p1646, 27p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 16
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Energies (19961073)
- Publication Type :
- Academic Journal
- Accession number :
- 162118984
- Full Text :
- https://doi.org/10.3390/en16041646