Abstract
Ash fouling has been an important factor in reducing 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 a 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. Then, a dynamic model is proposed to predict ash deposits in the coal-fired boilers by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and nonlinear autoregressive neural networks (NARNN). To construct a reasonable network model, the minimum information criterion and trial-and-error method are used to determine the delay orders and hidden layers. Finally, the experimental object is established on the 300 MV 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. In addition, the experimental results show that this multiscale prediction model is more competitive than the Elman model.
Highlights
Elena Gaudioso VázquezFossil fuel power plants play an important role in providing energy all over the world, even though renewable power has been greatly developed in recent decades
This paper presents a dynamic NN method, which uses Elman neural network (Elman) with neural feedback characteristics and nonlinear autoregressive neural network (NARNN) to build the model to predict the ash fouling of heating surfaces in coal-fired power plant boilers
The prediction of energy efficiency of heat transfer surfaces considering ash fouling plays an important role in Prognostics and Health Management (PHM) of coal-fired power plant boilers
Summary
Fossil fuel power plants play an important role in providing energy all over the world, even though renewable power has been greatly developed in recent decades. It is necessary to monitor the ash fouling of heat transfer surfaces and formulate corresponding soot blowing strategies to ensure the safety, reliability, and economy of coal-fired power plant boilers. PHM of ash fouling of heating surfaces in coal-fired power plant boilers is mainly divided into three processes, i.e., ash fouling monitoring, ash fouling prediction, and soot blowing optimization. Energies 2021, 14, 4000 calculation is too long to achieve real-time prediction, which is not suitable for real-time monitoring of the ash fouling on the heating surfaces of coal-fired power plant boilers. This paper presents a dynamic NN method, which uses Elman neural network (Elman) with neural feedback characteristics and nonlinear autoregressive neural network (NARNN) to build the model to predict the ash fouling of heating surfaces in coal-fired power plant boilers.
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