Soot Blowing Optimization for Frequency in Economizers to Improve Boiler Performance in Coal-Fired Power Plant

Shi, Yuanhao; Li, Qiang; Wen, Jinyu; Cui, Fangshu; Pang, Xiaoqiong; Jia, Jianfang; Zeng, Jianchao; Wang, Jingcheng

doi:10.3390/en12152901

Cited by 13 publications

(11 citation statements)

References 20 publications

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“…The time threshold of 300-time points of the experimental set data is [0, 8.33], that is, when N=250 and 200, the predictable time range is [6.94, 8.33] and [5.55, 8.33], respectively, and the time length is 1.39h and 2.78h. According to reference [18], when the soot deposition time is 7.62h (i.e. the fouling rate of heating surface reaches 0.36), the soot blowing starts.…”

Section: Results Analysismentioning

confidence: 99%

“…In the calculation of the heating surface of the boiler unit, some parameters (thermal resistance of ash and dirt, cleaning factor, etc.) that can represent the cleaning state of the heating surface are usually used to represent the cleaning state of the heating surface [18] . Generally, these parameters are the characteristic parameters that can be directly measured or observed during the normal operation of some boilers, which are obtained through mathematical modeling, that is, the ash monitoring process is the parameter modeling process that represents the ash pollution degree of the heating surface [19] .…”

Section: Principle Of Soot Accumulation Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Pollution State of Heating Surface in Coal-Fired Utility Boilers

Yan

Liu

et al. 2020

IEEE Access

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Section: Results Analysismentioning

confidence: 99%

Section: Principle Of Soot Accumulation Monitoringmentioning

confidence: 99%

Prediction of Pollution State of Heating Surface in Coal-Fired Utility Boilers

Yan

Liu

et al. 2020

IEEE Access

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“…Combined with the data available from the power station and the online monitoring model to calculate the ash deposition status. This monitoring method does not require special monitoring equipment and complicated calculation and aims at reducing steam consumption to design soot blowing optimization strategy [10]. Tong et al [11] established the thermal resistance of the ash layer to reflect the degree of ash accumulation.…”

Section: Introductionmentioning

confidence: 99%

A Neural-Network-Based Method for Ash Fouling Prediction of Heat Transfer Surface in Coal-Fired Power Plant Boiler

Shi

Cui

et al. 2021

IEEE Access

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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)

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“…Because the thermal resistance of the ash is much greater than that of the metal heating surface, to heat the working fluid will inevitably cause more raw coal consumption, which may further cause the corrosion of the heating surface and the metal pipeline and significantly reduce the service life of the equipment. In addition, due to the poor heat absorption capacity of the heating surface, the boiler tail outlet smoke temperature is high, so the flue Energies 2021, 14, 4000 2 of 19 gas desulfurization rate is reduced [2,3]. It has been estimated that ash deposition can reduce the amount to 1 percent of boiler efficiency under the normal operating conditions in coal-fired power plant boilers [4].…”

Section: Introductionmentioning

confidence: 99%

Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling

Shi

Wen

et al. 2021

Energies

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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.

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Soot Blowing Optimization for Frequency in Economizers to Improve Boiler Performance in Coal-Fired Power Plant

Cited by 13 publications

References 20 publications

Prediction of Pollution State of Heating Surface in Coal-Fired Utility Boilers

Prediction of Pollution State of Heating Surface in Coal-Fired Utility Boilers

A Neural-Network-Based Method for Ash Fouling Prediction of Heat Transfer Surface in Coal-Fired Power Plant Boiler

Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling

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