Abstract
Soot blowing optimization and health management of coal-fired power plant boiler has received increasing attention in recent years. The ash fouling monitoring and prediction are the basis for achieve this goal. Nowadays, with the development of neural network technology, the new data-driven methodologies are provided for ash fouling monitoring and prediction. This paper presents a comprehensive method based on neural-network for ash fouling prediction. Firstly, the health factor-clearness factor of the heated surface was established from the actual heat transfer coefficient and the theoretical heat transfer coefficient. Wavelet threshold denoising algorithm will be used as data preprocessing method. Secondly, use Ensemble Empirical Mode Decomposition (EEMD) to obtain a series of frequency stable parts. Finally, Encoder-Decoder based Attention (EDA) is used to predict the ash deposit on the heat transfer surface. The EDA model consists of an encoder, a decoder and an attention mechanism. The encoder and decoder are composed of Bidirectional Long Short-Term Memory (BI-LSTM) and Long Short-term Memory Network (LSTM) respectively. The function of the attention mechanism is that the output of each time step of the encoder is given a different degree of attention, and it is sent to the decoder as an attention vector after a weighted average operation. Ash accumulation data on the heat transfer surface of various devices are used to verify the effectiveness of the proposed hybrid model. In addition, the experimental results show that this method has better prediction accuracy than other variant models.
Highlights
New energy generation, such as wind and solar energy, has a promising future, but thermal power generation is still the power supply mode in most countries at the present stage [1,2]
Ash will be deposited on the heated surface of almost all the devices mentioned above, due to the poor thermal conductivity of the ash, the increase in ash accumulation will cause a reduction in the overall heat transfer efficiency of the boiler [6]
For the dataset used in this article, we introduce Root Mean Square Error (RMSE), Mean Square Error (MAPE), and Mean Absolute Error (MAE) as model evaluation indicators
Summary
New energy generation, such as wind and solar energy, has a promising future, but thermal power generation is still the power supply mode in most countries at the present stage [1,2]. Support vector regression (SVR) and Encoder-Decoder based Attention (EDA) are used to train and predict the lowfrequency and high-frequency signals respectively, which greatly improves the accuracy of prediction This datadriven method does not require complex, special instruments and computing systems, and can predict the health of the heated surface of coal-fired boilers only by using the readily available monitoring data. If the ash accumulation state of the heating surface has reached the critical threshold, there will be no extra time to conduct preparation operation and personnel allocation of soot blowing, and the best soot blowing time will be missed To solve such problems, rolling prediction is generally adopted. The main innovations and contributions are as follows: (1) Aiming at the past fixed-time and fixed-frequency soot blowing operations, this paper proposes the ash prediction of boiler heated surface based on a deep learning model., using an encoder-decoder-attention mechanism architecture. The experimental results verify the superiority and robustness of the model, as well as its good adaptability to a variety of datasets, and it provides a promising tool for the ash cleaning of coalfired power stations
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