Experimental and numerical investigation on heat transfer in the vertical upward flow water wall of a 660 MW ultra-supercritical CFB boiler

Zhou, Xuhao; Niu, Tiantian; Xin, Yafei; Li, Yinlong; Yang, Dong

doi:10.1016/j.applthermaleng.2021.116664

Cited by 15 publications

(2 citation statements)

References 29 publications

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“…Because of the complex combustion process of CFB units, much literature uses the first-order differential equation to model [31,32]. Inspired by these studies, this paper takes the differential value of variables as an additional input feature.…”

Section: Coal Slime Blending Circulating Fluidised Bed Overview and D...mentioning

confidence: 99%

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

2022

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Coal slime blending can effectively improve the utilization rate of fossil fuels and reduce environmental pollution. However, the combustion in the furnace is unstable due to the empty pump phenomenon during the coal slurry transport. The combustion instability affects the material distribution in the furnace and harms the unit operation. The bed pressure in the circulating fluidized bed unit reflects the amount of material in the furnace. An accurate bed pressure prediction model can reflect the future material quantity in the furnace, which helps adjust the operation of the unit in a timely fashion. Thus, a deep learning-based prediction method for bed pressure is proposed in this paper. The Pearson correlation coefficient with time correction was used to screen the input variables. The Gaussian convolution kernels were used to implement the extraction of inertial delay characteristics of the data. Based on the computational theory of the temporal attention layer, the model was trained using the segmented approach. Ablation experiments verified the innovations of the proposed method. Compared with other models, the mean absolute error of the proposed model reached 0.0443 kPa, 0.0931 kPa, and 0.0345 kPa for the three data sets, respectively, which are better than those of the other models.

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Section: Coal Slime Blending Circulating Fluidised Bed Overview and D...mentioning

confidence: 99%

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

2022

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“…To explore the cause of overtemperature in industrial boilers, researchers have considered different techniques. For example, refs − considered the influences of process variables like secondary air distribution, pulverized coal particle size, primary air pressure, and boiler load through experimental studies, to better understand the mechanism for boiler overtemperature. These experimental methods are of great help to understand the influence of operating variables on the water wall temperature; however, they are difficult to be applied due to the high complexity of the industrial boiler system.…”

Section: Introductionmentioning

confidence: 99%

Isolation of Overtemperature Fault in an Industrial Boiler Using Tree-Structured Sparsity-Based Reconstruction

Dong

Yao

Zeng

et al. 2022

Ind. Eng. Chem. Res.

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This paper develops a data-driven method for the isolation of overtemperature fault in an industrial boiler using the tree-structured sparsity technique, which relies on reconstruction-based contribution of principal component analysis (PCA). Unlike conventional fault tree analysis-based approaches, the developed method incorporates fault tree structure into the reconstruction objective of PCA in the form of tree-structured sparsity regularization term so that data model and process structure can be integrated. The tree-structured sparsity regularization term allows the selection of fault variables in a group-wise way along the fault tree, resulting in sparse and interpretable fault isolation. For the purpose of online application, this paper further designed an updating procedure for the regularization parameter and introduced a fast alternating multiplier method (FADMM) for parameter optimization. Application results on real data from an industrial boiler show that the proposed method outperforms competitive methods. In addition, by virtue of the fault tree structure, the developed method is able to provide useful information for root cause diagnosis and fault propagation analysis.

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Oxidation behavior of Al0.2CoCrFeNi high-entropy alloy film in supercritical water environment

et al. 2021

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Experimental and numerical investigation on heat transfer in the vertical upward flow water wall of a 660 MW ultra-supercritical CFB boiler

Cited by 15 publications

References 29 publications

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

Isolation of Overtemperature Fault in an Industrial Boiler Using Tree-Structured Sparsity-Based Reconstruction

Oxidation behavior of Al0.2CoCrFeNi high-entropy alloy film in supercritical water environment

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