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U.S. Department of Energy
Office of Scientific and Technical Information

Boiler Health Monitoring Using a Hybrid First Principles-Artificial Intelligence Model

Technical Report ·
DOI:https://doi.org/10.2172/2568166· OSTI ID:2568166
 [1];  [2];  [1];  [1];  [1];  [2];  [3]
  1. West Virginia Univ., Morgantown, WV (United States)
  2. Electric Power Research Inst. (EPRI), Palo Alto, CA (United States)
  3. Southern Company Services, Birmingham, AL (United States)
Due to increased penetration of the intermittent renewables to the grid, pulverized coal (PC) plants are being forced to cycle their load frequently and rapidly, operate at low load condition for sustained period, and start up and shut down several hundred times in a year in the worst case. These severe operations are causing substantial damage to the boiler components compromising the reliability of PC plants. An online health monitoring tool can be instrumental in understanding the impacts of load-following and can eventually help PC plants to develop advanced process control strategies for improved flexibility without compromising safety nor reliability.
Research Organization:
West Virginia Univ., Morgantown, WV (United States)
Sponsoring Organization:
USDOE Office of Fossil Energy and Carbon Management (FECM)
DOE Contract Number:
FE0031768
OSTI ID:
2568166
Report Number(s):
DOE-WVURC--FE0031768
Country of Publication:
United States
Language:
English

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