TY - JOUR
TI - Ash fouling monitoring and key variables analysis for coal fired power plant boiler
AU - Shi Yuanhao
AU - Wang Jingcheng
JN - Thermal Science
PY - 2015
VL - 19
IS - 1
SP - 253
EP - 265
PT - Article
AB - Ash deposition on heat transfer surfaces is still a significant problem in  coal-fired power plant utility boilers. The effective ways to deal with this  problem are accurate on-line monitoring of ash fouling and soot-blowing. In  this paper, an online ash fouling monitoring model based on dynamic mass and  energy balance method is developed and key variables analysis technique is  introduced to study the internal behavior of soot-blowing system. In this  process, artificial neural networks (ANN) are used to optimize the boiler  soot-blowing model and mean impact values method is utilized to determine a  set of key variables. The validity of the models has been illustrated in a  real case-study boiler, a 300MW Chinese power station. The results on same  real plant data show that both models have good prediction accuracy, while  the ANN model II has less input parameters. This work will be the basis of a  future development in order to control and optimize the soot-blowing of the  coal-fired power plant utility boilers.