Experimental research on convection–condensation heat transfer characteristics and sootblowing operation strategy of waste heat recovery heat exchanger for dryer exhaust

Qin, Yuanzhi; Wang, Jinshi; Liu, Yiwen; Duan, Yifei; Liu, Ming; Yan, Junjie

doi:10.1080/07373937.2018.1528452

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…As an effective method to keep the heated surface healthy, soot blowing is used to clean the surface of the heat exchanger through a medium such as high temperature steam. Nowadays, many thermal power stations all over the world adopt the soot blowing method at a fixed time and a fixed operation process. , This soot blowing method has such a hidden problem: If soot blowing is not timely (under soot blowing), it will lead to aggravation of the ash situation in the heated area, reduction of heat transfer efficiency, and major safety accidents. If the soot blowing frequency is too high (over soot blowing), it will not only cause waste of high-temperature and pressure steam used for soot blowing but also cause corrosion of the heated surface and pipeline.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Shi

Wen

et al. 2022

ACS Omega

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Failure to blow ash on the heated surface of the boiler will cause a drop in heat transfer rate and even industrial safety accidents. Nowadays, the shortcomings of the fixed soot blowing operation every hour and every shift are significant, which can be improved by high-precision ash accumulation prediction. Therefore, this paper proposes a deep learning model fused with deep feature extraction. First, a dynamic fouling model and a health index-clearness factor ( CF ) of the heated surface are established. The data preprocessing method reduces unnecessary forecasting difficulty and makes the degradation trend of the CF time series more obvious. In addition, deep feature extraction is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and kernel principal component analysis (KPCA), which completes the multiscale analysis of time series and reduces the training time of deep learning models, and has significant contributions to improving prediction accuracy and reducing time consumption. The adaptive sliding window and the encoder–decoder based on the attention mechanism (EDA) can better mine the internal information of the time series. Compared with long short-term memory (LSTM), taking the 300 MW boiler’s various heated surface data sets as an example, multistep forward prediction and different starting point prediction experiments have verified the superiority and effectiveness of the model. Finally, under the variable working condition economizer datasets, the proposed method better completes the predictive maintenance task of the heated surface. The research results provide operational guidance for improving heat transfer rate, energy saving, and reducing consumption.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Shi

Wen

et al. 2022

ACS Omega

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“…Reducing the intensity of low‐temperature fouling can improve the ash removal rate by soot blower, reduce soot‐blowing duration, and effectively ensure the normal operation of boilers 38,39 . During soot blowing, higher fouling deposition strength requires higher purging medium (such as steam) pressure or power and a longer duration for completely removing the fouling, which will lead to the decline of boiler economy and the wear of the heating surface 40,41 . Therefore, it is quite necessary to study the strength characteristics of low‐temperature fouling on the heat recovery devices and contribute to an insight into the strength reduction mechanism of low‐temperature fouling.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of the strength characteristics of low‐temperature fouling containing ammonium bisulfate in boilers

Tang

Zhang

Wang

et al. 2022

Asia-Pacific J Chem Eng

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Low-temperature fouling is a severe problem in the heat recovery devices of coal-fired boilers; it is mainly caused by the condensation of ammonium bisulfate (ABS, NH 4 HSO 4 ) and is usually challenging to remove with soot-blowing equipment. This study investigates the strength of low-temperature fouling containing ABS, which is an essential indicator in soot blowing. The ash sample containing ABS with three different mass ratios was heated in a drop tube furnace (DTF) at several selected temperature conditions for different times.The compressive strength of ash blocks after heating was measured, and the energy-dispersive X-ray spectrometry (EDX) and scanning electron microscope (SEM) were used to clarify the relationship between ABS content with fouling strength. The results show that the higher content of ABS means higher fouling strength. The fouling strength could decrease by heating the fouling under the flue gas temperature is 350 C or more, and a higher heating temperature could result in lower strength of the fouling. To reduce the strength and maximize the removal of fouling effectively, taking into account the economy, the recommended heating condition is to heat the fouling for 5-20 min at a minimum temperature of 350 C.

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Flue Gas Water Recovery by Indirect Cooling Technology for Large-Scale Applications: A Review

Zhong

Cao

et al. 2020

J. Therm. Sci.

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Experimental research on convection–condensation heat transfer characteristics and sootblowing operation strategy of waste heat recovery heat exchanger for dryer exhaust

Cited by 7 publications

References 46 publications

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Experimental investigation of the strength characteristics of low‐temperature fouling containing ammonium bisulfate in boilers

Flue Gas Water Recovery by Indirect Cooling Technology for Large-Scale Applications: A Review

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