Development of flame monitoring system with optical receiver for pulverized coal firing boilers

2005 ◽  
Author(s):  
Seung-Yeob Baeg ◽  
Seung-Min Kim ◽  
Chang-Ho Cho
Keyword(s):  
Monitoring System ◽  
Pulverized Coal ◽  
Optical Receiver ◽  
Flame Monitoring

FLAME MONITORING SYSTEM OF POWER STATION PLANT - A SURVEY

2018 ◽  
Vol 5 (1) ◽  
pp. 33
Author(s):  
C.MANE MAMTA ◽  
J.S. KULKARNI ◽  
◽  
Keyword(s):  
Monitoring System ◽  
Power Station ◽  
Flame Monitoring

Application of fiber optic flame monitoring system for estimation burner input parameters

2014 ◽  
Author(s):  
Andrzej Kotyra ◽  
Waldemar Wójcik ◽  
Sarsenbek Zhussupbektov ◽  
Aigul Iskakova
Keyword(s):  
Monitoring System ◽  
Fiber Optic ◽  
Input Parameters ◽  
Flame Monitoring

Application of flame monitoring system for flame spectrum analysis in industrial conditions

2004 ◽  
Author(s):  
Waldemar Wojcik ◽  
Tomasz Golec ◽  
Andrzej Kotyra ◽  
Slawomir Cieszczyk ◽  
Mariusz Duk ◽  
...  
Keyword(s):  
Monitoring System ◽  
Spectrum Analysis ◽  
Flame Monitoring

Visual flame monitoring system based on two-color method

2009 ◽  
Vol 18 (3) ◽  
pp. 284-288 ◽  
Author(s):  
Fan Jiang ◽  
Shi Liu ◽  
Shiqiang Liang ◽  
Zhihong Li ◽  
Xueyao Wang ◽  
...  
Keyword(s):  
Monitoring System ◽  
Flame Monitoring

Concept of application of signals from fiber optic system for flame monitoring to control separate pulverized coal burner

2003 ◽  
Author(s):  
Waldemar Wojcik ◽  
Tomasz Golec ◽  
Pawel Komada ◽  
Slawomir Przylucki ◽  
Andrzej Smolarz ◽  
...  
Keyword(s):  
Fiber Optic ◽  
Pulverized Coal ◽  
Optic System ◽  
Fiber Optic System ◽  
Flame Monitoring ◽  
Pulverized Coal Burner

An intelligent monitoring system for the detection of slag deposition on a pulverized coal fired burner

2005 ◽  
Vol 219 (5) ◽  
pp. 383-394 ◽  
Author(s):  
C. K. Tan ◽  
S. J. Wilcox ◽  
J Ward ◽  
M Lewitt
Keyword(s):  
Monitoring System ◽  
Pulverized Coal ◽  
Combustion Noise ◽  
Self Organizing Map ◽  
Hybrid Neural Network ◽  
Intelligent Monitoring ◽  
Cast Refractory ◽  
High Degree ◽  
Further Development ◽  
Large Deposits

The objective of this paper is to describe the further development of a monitoring system to detect the presence of so-called burner eyebrows, i.e. relatively large deposits of slag around the burner quarl in pulverized coal fired boilers. Experiments were undertaken with a range of coals and with various artificial eyebrows constructed from cast refractory inserts. The system uses a microphone to detect combustion noise and an infrared sensor which measures flame radiation, and the signals from these cheap, easily installed sensors were analyzed by a hybrid neural network. In tests with two coals, the system was able to distinguish the different eyebrows with a high degree of accuracy if representative data were used to train the network for each particular coal. In further tests with a range of six different coals, the system was able to distinguish between a clean burner and one fitted with a particular sized eyebrow. In this case, it proved to be possible to use only the features from three of the coals in the training process and the data from the remaining fuels for validation. The monitoring system, therefore, appears to be relatively independent of changes to the coal fired by the burner if trained with a representative range of coals. Finally, this paper presents a possible method to detect burner eyebrows via the evaluation of so-called ‘eyebrow indices’ using a self-organizing map which is trained solely using clean burner sensor patterns.

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