Leak detection in a subcritical boiler

Panday, Rupen; Indrawan, Natarianto; Shadle, Lawrence J.; Vesel, Richard W.

doi:10.1016/j.applthermaleng.2020.116371

Title: Leak detection in a subcritical boiler

Journal Article · Fri Nov 27 00:00:00 EST 2020 · Applied Thermal Engineering

DOI:https://doi.org/10.1016/j.applthermaleng.2020.116371· OSTI ID:1764392

Panday, Rupen ^[1]; Indrawan, Natarianto ^[1]; Shadle, Lawrence J. ^[2]; Vesel, Richard W. ^[3]

National Energy Technology Lab. (NETL), Morgantown, WV (United States); Leidos Research Support Team, Morgantown, WV (United States)
National Energy Technology Lab. (NETL), Morgantown, WV (United States)
ABB, Inc., Cleveland, OH (United States)

Thermal power plants experience cycling duty leading to the fatigue of the boiler and heat exchanger tubes. As a result, tube failures occur frequently in coal fired fleets leading to forced outages. Furthermore, because the tube leaks have been the major source of unwanted shutdowns and the number of outages is increasing, present work focuses on the detection and isolation of the leak in a subcritical boiler based upon the process data from a commercial coalfired power plant. The mass balance equation around the steam drum was analyzed using timeseries data collected from a 300 MW power plant. The ratio of the feed water mass flow rate to the steam mass flow rate was defined as a key parameter for detecting leaks. The difference in slope between the feedwater and steam mass flow rate during the normal and faulty operations was established as the upper control limit for real time monitoring. To reduce false alarm rates that arise when raw signal is directly compared against the threshold due to common process fluctuations, an optimal filter was derived for smoothing. It was found that the optimal filter reacted much more quickly to process changes than an exponential moving average filter, around 8 h earlier on average. Occurrence of relatively high false alarm rates even in the filtered responses was related to the cycling of the boiler from the base load condition. Variable threshold was established to keep false alarm rates to the minimum while maintaining the leak detection rate. Finally, the leak was located at the economizer and this could readily be isolated by investigating the magnitude of the mass flow rates ratio and the temperature at the economizer outlet.

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Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: 89243318CFE000003

OSTI ID:: 1764392

Journal Information:: Applied Thermal Engineering, Vol. 185; ISSN 1359-4311

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (15)

Detection of Tube Leaks and Their Location Using Input/Loss Methods Lang, Fred D.; Rodgers, David A. T.; Mayer, Loren E. ASME 2004 Power Conference https://doi.org/10.1115/POWER2004-52027	conference	November 2008
Online performance monitoring platform based on the whole process models of subcritical coal-fired power plants Tian, Zhen; Xu, Liang; Yuan, Jingqi Applied Thermal Engineering, Vol. 124 https://doi.org/10.1016/j.applthermaleng.2017.06.112	journal	September 2017
On-line monitoring the performance of coal-fired power unit: A method based on support vector machine Cai, Jiejin; Ma, Xiaoqian; Li, Qiong Applied Thermal Engineering, Vol. 29, Issue 11-12 https://doi.org/10.1016/j.applthermaleng.2008.11.012	journal	August 2009
Power station boiler furnace water-cooling wall tube leak locating method based on acoustic theory Zhang, Shiping; Shen, Guoqing; An, Liansuo Applied Thermal Engineering, Vol. 77 https://doi.org/10.1016/j.applthermaleng.2014.12.015	journal	February 2015
A new instrument for radiation heat flux measurement—analysis and parameter selection Martins, N.; Carvalho, M. G.; Afgan, N. H. Heat Recovery Systems and CHP, Vol. 15, Issue 8 https://doi.org/10.1016/0890-4332(94)00082-V	journal	November 1995
Boiler tube leakage detection expert system Afgan, N.; Coelho, P. J.; Carvalho, M. G. Applied Thermal Engineering, Vol. 18, Issue 5 https://doi.org/10.1016/S1359-4311(97)00054-9	journal	January 1998
Efficient model-based leak detection in boiler steam-water systems Sun, Xi; Chen, Tongwen; Marquez, Horacio J. Computers & Chemical Engineering, Vol. 26, Issue 11 https://doi.org/10.1016/S0098-1354(02)00147-3	journal	November 2002
An improved PCA method with application to boiler leak detection Sun, Xi; Marquez, Horacio J.; Chen, Tongwen ISA Transactions, Vol. 44, Issue 3 https://doi.org/10.1016/S0019-0578(07)60211-0	journal	July 2005
Bayesian network-based early-warning for leakage in recovery boilers Widarsson, Björn; Dotzauer, Erik Applied Thermal Engineering, Vol. 28, Issue 7 https://doi.org/10.1016/j.applthermaleng.2007.06.016	journal	May 2008
Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling Li, Fan; Upadhyaya, Belle R.; Coffey, Lonnie A. ISA Transactions, Vol. 48, Issue 2 https://doi.org/10.1016/j.isatra.2008.10.014	journal	April 2009
A data reconciliation based framework for integrated sensor and equipment performance monitoring in power plants Jiang, Xiaolong; Liu, Pei; Li, Zheng Applied Energy, Vol. 134 https://doi.org/10.1016/j.apenergy.2014.08.040	journal	December 2014
Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks Rostek, Kornel; Morytko, Łukasz; Jankowska, Anna Energy, Vol. 89 https://doi.org/10.1016/j.energy.2015.06.042	journal	September 2015
Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants Hundi, Prabhas; Shahsavari, Rouzbeh Applied Energy, Vol. 265 https://doi.org/10.1016/j.apenergy.2020.114775	journal	May 2020
Using SVM based method for equipment fault detection in a thermal power plant Chen, Kai-Ying; Chen, Long-Sheng; Chen, Mu-Chen Computers in Industry, Vol. 62, Issue 1 https://doi.org/10.1016/j.compind.2010.05.013	journal	January 2011
Neural Networks and the Bias/Variance Dilemma Geman, Stuart; Bienenstock, Elie; Doursat, René Neural Computation, Vol. 4, Issue 1 https://doi.org/10.1162/neco.1992.4.1.1	journal	January 1992

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