Prediction of Ash Deposition on Heating Surfaces of Coal-fired Power Plant Boiler based on Dynamic Neural Network

Ash fouling has been an important factor to reduce the heat transfer efficiency and safety of the coal-fired power plant boilers. Scientific and accurate prediction of ash fouling of heat transfer surfaces is the basis of formulating reasonable soot blowing strategy to improve energy efficiency. This study presented a comprehensive approach of dynamic prediction of the ash fouling of heat transfer surfaces in coal-fired power plant boilers. At first, the cleanliness factor is used to reflect the fouling level of the heat transfer surfaces. And then, a dynamic model is proposed to predict ash deposits in the coal-fired boilers using combining complete ensemble empirical mode decom-position with adaptive noise (CEEMDAN) and nonlinear autoregressive neural networks (NARNN). Finally, the experimental object is established on the 300MV economizer clearness factor dataset of the power station, and the root mean square error and mean absolute percentage error of the proposed method are the smallest.