Work

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

Year: 2024

Type: article

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.

Source: Energies

DOI: 10.3390/en17225571

Authors: Angjun Xie, Gang Xu, Chunming Nie, Heng Chen, Tailaiti Tuerhong

Institutions: North China Electric Power University, State Power Investment Corporation (China)

Language: English

Topic: Thermodynamic and Exergetic Analyses of Power and Cooling Systems

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $2,955