Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms

Angjun Xie, Gang Xu, Chunming Nie, Heng Chen () and Tailaiti Tuerhong
Additional contact information
Angjun Xie: School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Gang Xu: School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Chunming Nie: State Power Investment Corporation Digital Technology Co., Ltd., Beijing 102200, China
Heng Chen: School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Tailaiti Tuerhong: School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2024, vol. 17, issue 22, 1-23

Abstract: Air-cooling technology has been widely used for its water-saving advantage, and the performance of air-cooled condensers (ACC) has an important impact on the operation status of the unit. In this paper, the performance of ACC in a typical coal-fired power plant is optimized by using machine learning (ML) algorithms. Based on the real operation data of the unit, this paper establishes a back pressure optimization model by using back propagation neural network (BPNN), random forest (RF), and genetic algorithm back propagation (GA-BP) methods, respectively, and conducts a comparative analysis of performance optimization and power-saving effect of the three algorithms. The results show that three algorithms offer significant power savings in the low-load section and smaller power savings in the high-load section. Moreover, when the ambient temperature is lower than 10 °C, the power-saving effect of the three algorithms after optimization is not much different; when the ambient temperature is greater than 10 °C, the power-saving effect of the performance optimization of BPNN and RF is significantly better than that of GA-BP. The optimization method has a good effect on improving the performance of ACC.

Keywords: coal-fired power plant; optimal back pressure; machine learning algorithms; operation data analysis; performance optimization (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/22/5571/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/22/5571/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:22:p:5571-:d:1516190

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().