A new model-based approach for power plant Tube-ball mill condition monitoring and fault detection

Shen Guo, Jihong Wang, Jianlin Wei, Paschalis Zachariades

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
266 Downloads (Pure)

Abstract

With the fast growth in intermittent renewable power generation, unprecedented demands for power plant operation flexibility have posed new challenges to the ageing conventional power plants in the UK. Adding biomass to coal for co-fired power generation has become widely implemented practices in order to meet the emission regulation targets. These have impacted the coal mill and power plant operation safety and reliability. The Vertical Spindle mill model was developed through the authors’ work before 2007. From then, the new research progress has been made in modelling and condition monitoring for Tube-ball mills and is reported in the paper. A mathematical model for Tube-ball milling process is developed by applying engineering principles combined with model unknown parameter identifications using a computational intelligent algorithm. The model describes the whole milling process from the mill idle status, start-up to normal grinding and shut-down. The model is verified using on-site measurement data and on-line test. The on-line model is used for mill condition monitoring in two ways: (i) to compare the predicted and measured mill output pressure and temperatures and to raise alarms if there are big discrepancies; and (ii) to monitor the mill model parameter variation patterns which detect the potential faults and mill malfunctions.
Original languageEnglish
Pages (from-to)10-19
Number of pages10
JournalEnergy Conversion and Management
Volume80
Early online date25 Jan 2014
DOIs
Publication statusPublished - 1 Apr 2014

Keywords

  • System modelling
  • Genetic Algorithms
  • Power generation control
  • coal fired power plants

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