This paper presents a comprehensive approach for optimization of soot-blowing of air preheaters in a coal-fired power plant boiler. In the method, modeling of the cleanliness factor is firstly proposed to monitor the ash deposition status of the air preheaters. Then, the statistical fitting of the ash fouling status is subsequently obtained to analyze the ash fouling dynamics and assessment of optimized soot-blowing strategies. Soot-blowing strategies are finally developed to optimize the steam consumption and heat transfer efficiency. Our methods can achieve the fouling monitoring and soot-blowing optimization of air preheater (APH) by using the existing monitoring data, not requiring additional special instruments and complex computing systems. The methodology is validated with the actual operating data of a 300 MW coal-fired power plant boiler. The results show the effectiveness of the proposed method. It can be used for the soot-blowing optimization in most coal-fired power plant boiler with air preheaters.

Because of the present ineffective method of soot blowing on a boiler’s heating surface in a coal-fired power plant, and to improve the economic benefit of the boiler in the power plant, weigh the improvement of boiler efficiency and steam loss brought by soot blowing, and ensure the safe operation of the unit, an optimization model of soot blowing on the boiler’s heating surface is established. Taking the economizer of the 300 MW coal-fired power plant unit as the research object, the measurement data and basic thermodynamic calculation data of the Distributed Control System (DCS) of the thermal power plant are used to calculate the fouling rate of the heated surface in real time. By analyzing the multi-group fouling rate under the same working conditions, the incremental distribution of the same measuring point at different times is obtained, and the expectation is obtained according to the distribution curve. The state of heating of the heated surface at a time in the future is predicted by the known initial cleaning state. By analyzing the trend of the fouling rate and combining the soot blowing optimization model, a set of soot blowing optimization strategies are proposed. The method proposed in this manuscript can be applied to the guidance of boiler soot blowing operation.

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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 BI-LSTM and 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.INDEX TERMS Soot blowing optimization, ash fouling prediction, ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM), encoder-decoder based attention (EDA)