A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants

Shangli Zhou, Hengjing He, Leping Zhang, Wei Zhao and Fei Wang ()
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Shangli Zhou: Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China
Hengjing He: Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China
Leping Zhang: Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China
Wei Zhao: Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China
Fei Wang: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2023, vol. 16, issue 4, 1-27

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.

Keywords: CO 2; emission; coal-fired power plant; deep learning; data-driven (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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