Influence of air ratio on combustion and NO<i><sub>x</sub></i> emission characteristics of pulverized coal industrial boiler

Yan, Ran; Chen, Zhichao; Liu, Yuan; Zhang, Bo; Li, Xin; Li, Zhengqi

doi:10.1080/00102202.2022.2050716

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…Therefore, a comprehensive, rapid, and accurate prediction of the slagging on the wall of the PCC of the boiler burner can provide timely guidance for the design of the boiler burner, so as to prevent accidents caused by the slagging of the boiler. The actual research shows that, − in addition to the static indicators, the influencing factors of boiler slagging degree are more importantly affected by the aerodynamic field and temperature field, and there is a complex nonlinear relationship between them. Based on the analysis of slagging on the heating surface of existing coal-fired boilers, it is concluded that the reasonable and accurate establishment of the prediction model for slagging on the heating surface is the basis and prerequisite for realizing the rapid identification of the degree of slagging on the wall.…”

Section: Introductionmentioning

confidence: 99%

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Zhang

Zhao

et al. 2023

ACS Omega

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The pre-combustion chamber (PCC) is commonly used to ensure stable combustion in boilers. However, when a coal-fired boiler uses a PCC combustor, the cross-sectional area and volumetric heat load in the PCC are high, which is prone to slagging, affecting the safe and stable operation of the boiler. Therefore, developing a fast and accurate prediction model is very important for judging the degree of slagging on the wall of the PCC. In recent years, artificial intelligence (AI) has been widely used in the field of thermal engineering, especially in the prediction of slagging. However, currently, using neural networks to predict the degree of boiler slagging only inputs simple parameters such as silicon ratio and acid–base ratio, without considering the actual complex flow and combustion characteristics in the furnace. In order to improve the accuracy of boiler slagging prediction, a deep parallel residual convolution neural network (DPRCNN) is proposed for automatically identifying three types of boiler wall slagging degrees. First, we simulate the boiler combustion process under various operating and structural parameters and output a dataset. Second, experimental validation is used to numerically simulate typical operating conditions, verifying the accuracy of the resulting dataset, and the generated dataset is sent to the DPRCNN model for identification. Finally, a convolutional neural network incorporating parallel thinking is proposed to predict boiler slagging. The experimental results show that the accuracy, accuracy, and area under curve (AUC) of DPRCNN reach 100%, 100%, and 100%, verifying the applicability of deep learning technology to boiler slagging prediction.

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Section: Introductionmentioning

confidence: 99%

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Zhang

Zhao

et al. 2023

ACS Omega

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“…Studies have shown that catalysts with carbon nanotubes as a carrier can affect the combustion performance of solid propellants, reduce the pressure index of solid propellants and increase the combustion rate. Meanwhile, CNTs themselves possess good catalytic activity [27][28][29] . The composite catalyst prepared by loading the burning-rate catalytically active species on CNTs can effectively solve the problems of easy agglomeration and difficult dispersion of nano-catalytically active materials during the preparation process, and the performance of CNTs and the laden catalytically active species is "complementary", so the prepared new composite catalytic material exhibits superior catalytic effect than pure nanocatalyst [30] .…”

Section: Introductionmentioning

confidence: 99%

The preparation of lead oxide / carbon nanotubes complex and its catalytic performance

Li¹,

Wei

Zhang³

et al. 2023

J. Phys.: Conf. Ser.

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The introduction of carbon nanotubes (CNTs) in the preparation of nano-lead oxide (PbO) can effectively solve the problem of easy agglomeration of nano-lead oxide. This study used concentrated nitric acid and concentrated sulfuric acid mixed acid (volume ratio 1:3) to oxidize carbon nanotubes at 90 °C temperature for 0.5 h. Using and the oxidized CNTs as carriers, Pb(OAc)2 · 3H2O as precursor, concentrated ammonia as a precipitator, PbO/CNTs complexes were prepared by chemical precipitation. Lead content of PbO/CNTs complex, determined by Complexometry, was 59.80 %(wt%), XRD and XPS characterizations revealed that the catalyst components of the prepared complex were mainly nano-sized PbO, And a small amount of lead, SEM and EDX characterization revealed PbO with the particle size between 20 m-200 nm had been successfully Loaded onto the CNTs surface, TEM show that the PbO and CNTS retained the original lattice, a spot of lead oxide was found inside the tubes. The thermal decomposition effect of PbO/CNTs on AP, RDX, HMX was evaluated by DSC, and the catalytic effect on AP and RDX was found to be obvious.

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Influence of air ratio on combustion and NO_x emission characteristics of pulverized coal industrial boiler

Cited by 2 publications

References 36 publications

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

The preparation of lead oxide / carbon nanotubes complex and its catalytic performance

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Influence of air ratio on combustion and NOx emission characteristics of pulverized coal industrial boiler

Cited by 2 publications

References 36 publications

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

The preparation of lead oxide / carbon nanotubes complex and its catalytic performance

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Influence of air ratio on combustion and NO_x emission characteristics of pulverized coal industrial boiler