Ash fouling monitoring and key variables analysis for coal fired power plant boiler

Abstract

Ash deposition on heat transfer surfaces is still a significant problem in coalfired 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 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 300 MW Chinese power station. The results on same real plant data show that both models have good prediction accuracy, while the artificial neural networks 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.