Soft-computing models for soot-blowing optimization in coal-fired utility boilers

Fouling and slagging are classical difficulties in pulverized fuel utility boilers, which cause important degradation and dramatic reduction of efficiency. Current cleaning devices, traditionally based on prefixed soot-blowing manoeuvres, mitigate the problem only partially and offers clear optimization possibilities. But the development of predictive control is far from easy: soot-blowing represents a significant fraction of efficiency, the effectiveness has an intrinsic level of randomness and the fouling dynamics involves nonlinear feedback loops. The complexity of the phenomenon makes not applicable theoretical models or statistical analysis. The problem requires the application of alternative methodologies as expert systems or soft-computing based techniques. The present paper aims to develop a probabilistic model to predict the effectiveness of soot-blowing based in Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems. The validity of the model has been illustrated in a real case-study boiler, a 350 MWe Spanish power station. Training and test data for these models are provided by a monitoring system based on heat-flux measurements in the furnace water-walls. For evaluation and comparison purposes, the quality of prediction obtained for the mentioned algorithms is analyzed in terms of performance indices. The connection weight approach reveals the relative importance of input variables in each soft-computing model. Finally, the integration of these models into an advisory tool is discussed.