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Journal of Chemical Engineering of Japan Volume 56, 2023 - Issue 1
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Research Paper

Fast Prediction of Heat Flux Distribution in Boilers Using Computational Fluid Dynamics Simulation Data via Multi-Extreme Learning Machines

Zhenhao TangSchool of Automation Engineering, The Northeast Electric Power University, Jilin132000, ChinaCorrespondence[email protected]
,
Yuan YangSchool of Automation Engineering, The Northeast Electric Power University, Jilin132000, China
,
Luyin PanSchool of Automation Engineering, The Northeast Electric Power University, Jilin132000, China
,
Mengxuan SuiSchool of Automation Engineering, The Northeast Electric Power University, Jilin132000, China
&
Shengxian CaoSchool of Automation Engineering, The Northeast Electric Power University, Jilin132000, China
Article: 2260416 | Received 16 Apr 2023, Accepted 14 Sep 2023, Published online: 04 Oct 2023

Figures & data

Figure 1. 350 MW boiler: (a) geometry model; (b) boiler meshing.

Figure 1. 350 MW boiler: (a) geometry model; (b) boiler meshing.

Table 1. Industrial analysis of coal quality.

Table 2. Elemental analysis of coal quality.

Table 3. Description of numerical simulation boundary conditions.

Figure 2. Heat flux distribution of furnace wall.

Figure 2. Heat flux distribution of furnace wall.

Figure 3. Framework of heat flux distribution fast prediction in boilers using computational fluid dynamics simulation data via multi-extreme learning machines.

Figure 3. Framework of heat flux distribution fast prediction in boilers using computational fluid dynamics simulation data via multi-extreme learning machines.

Table 4. Classification of 120 operating conditions.

Figure 4. Structure of ELM network for prediction of heat flux distribution.

Figure 4. Structure of ELM network for prediction of heat flux distribution.

Table 5. MMAE values of the six prediction models.

Figure 5. Prediction error distribution of T10 operating condition.

Figure 5. Prediction error distribution of T10 operating condition.

Table 6. The hyperparameter settings of the comparison model.

Figure 7. 3D simulation results of different modeling algorithms under T10 operating condition.

Figure 7. 3D simulation results of different modeling algorithms under T10 operating condition.

Table 7. Comparison of experimental results with different algorithms.

Figure 8. Prediction results with different algorithms under different loads.

Figure 8. Prediction results with different algorithms under different loads.

Table 8. Model evaluation with different algorithms under different loads.

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