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Issue title: High-Performance Computing
Guest editors: Achyut Shankar
Article type: Research Article
Authors: Gao, Lipinga | Lin, Zhouyonga | Lian, Dadea | Lin, Yilonga | Yang, Kaixuanb; * | Li, Zhib | Li, Letianb
Affiliations: [a] Chn Energy (Quanzhou) Thermal Power Co., Ltd., Quanzhou, Fujian, China | [b] Shanghai Power Equipment Research Institute Co., Ltd., Shanghai, China
Correspondence: [*] Corresponding author: Kaixuan Yang, Shanghai Power Equipment Research Institute CO., LTD., Shanghai 200240, China. E-mail: ylong_lin@163.com.
Abstract: High performance computing is a theory, method, and technology that utilizes supercomputers to achieve parallel computing, and is widely applied in various fields. The problem of thermal deviation and overheating tube explosion on the high-temperature heating surface of coal-fired boilers in large power plants is becoming increasingly prominent, seriously threatening the safe operation of power plant units. In order to solve these problems, an intelligent monitoring method for the overheating status of coal-fired boilers in large power plants is proposed. Analyze the damage mechanism of the high-temperature heating surface of the boiler, and monitor in real-time the temperature exceeding the limit of the heating surface; Constructing gas-phase turbulent combustion models and metal wall temperature models; Analyze the energy consumption loss during the operation of coal-fired boilers, calculate the heat transfer function of the boiler, and complete the intelligent monitoring of the overheating status of the boiler heating surface. The experimental results show that the rationality of the research method is optimal at a relative height of 100 m; The equivalent heat flux density of the boiler has been improved, and the ability to control consumption losses is better; It can improve the wind power consumption capacity of the heating system.
Keywords: Large power station, coal-fired boiler, heating surface, overtemperature state, intelligent monitoring method
DOI: 10.3233/IDT-240502
Journal: Intelligent Decision Technologies, vol. 18, no. 4, pp. 3203-3217, 2024
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