Failure Analysis on Spring of Boiler Drum Safety Valve

2012 ◽  
Vol 217-219 ◽  
pp. 2279-2282
Author(s):  
Yan Ting Feng ◽  
Peng Sun ◽  
Xue Xia Xu ◽  
Hong Bin Chen
Keyword(s):  
Safety Valve ◽  
Material Selection ◽  
Hardness Test ◽  
Composition Analysis ◽  
Temper Brittleness ◽  
Microstructure Observation ◽  
Power Plant Boiler ◽  
Boiler Operation ◽  
Boiler Drum ◽  
Plant Boiler

Safety valve is an important component of a boiler. The failure of its spring will bring hidden trouble to the boiler operation. The fracture failure of a drum safety valve spring of a power plant boiler was discussed based on chemical composition analysis, hardness test, metallurgical microstructure observation and SEM. Results showed that improper material selection and improper heat treatment process which led to temper brittleness contributes to the fracture. In addition, oxygen corrosion pit on the surface of the spring play an important role in promoting the cracking.

Power Plant Boiler Failure Analysis of Main Components

2014 ◽  
Vol 945-949 ◽  
pp. 1073-1076 ◽  
Author(s):  
Hong Liang Xia
Keyword(s):  
Power Plant ◽  
Failure Analysis ◽  
Analysis Method ◽  
Power Plant Boiler ◽  
Boiler Operation ◽  
Main Components ◽  
Power Station Boiler ◽  
Failure Types ◽  
Plant Boiler

This paper briefly introduces the basic condition of power station boiler operation and failure analysis of the necessity of power plant boiler failure types and main components of power plant boiler failure types of main components. Illustrates the failure analysis of the main analysis method, and introduces the failure analysis of the role of failure analysis.

Soft Measuring Model Based on CMAC Artificial Neural Network for Pollutants Release from Coal Combusting in Power Plant Boiler

2012 ◽  
Vol 518-523 ◽  
pp. 2192-2195
Author(s):  
Yong Zheng Wang ◽  
Lei Jiang ◽  
Sheng Xue Chen ◽  
Chun Mei Lu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Plant ◽  
Pollutant Emission ◽  
Power Plant Boiler ◽  
Boiler Operation ◽  
Pollutants Emission ◽  
On Line ◽  
Artificial Neural ◽  
Plant Boiler

The soft measuring model, for pollutants release from coal combusting in power plant boiler, was combined with the test data obtained from boiler operation, established on the basis of technology for CMAC artificial neural network, and adopted the hyper CMAC structure and algorithm. The characteristic data about coal quality from boiler operation and combusting conditions in the furnace were took as its input parameters, to achieve precise prediction and on-line measurement of concentration of sulfur and nitrogen pollutant emission. It was effective to guide operator in the power plant to optimize combustion and control pollutants emission.

Sign in / Sign up

Export Citation Format

Share Document