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• W4386574039 endingPage "113507" @default.
• W4386574039 startingPage "113507" @default.
• W4386574039 abstract "Predicting furnace temperature distribution is vital for coal-fired boiler safety. Existing methods, including finite element calculations and three-dimensional (3D) reconstruction still face limitations. A 3D combustion temperature field prediction method, GDNN, which utilizes offline-computed computational fluid dynamics (CFD) simulation results for online predictions of entire boiler, was proposed. GDNN method leverages the knowledge of the temperature field acquired by the base neural network model and gaussian processes, and utilizes probabilistic calculation to determine the predicted output as the maximum probability of temperature value. Experimental results demonstrate that the proposed method effectively avoids overfitting and exhibits the highest prediction accuracy among the comparative algorithms. Furthermore, a temperature field correction method is introduced, which employs intermediate variables of the GDNN model and measured values from temperature sensors to establish a correction model for the entire predicted temperature field. Experiments show that this correction effectively improves the overall boiler temperature field, particularly in areas such as the final superheater, final reheater, and economizer. The optimal parameters for predicting and correcting 3D furnace temperature field results were determined through experimental comparison, and the proposed method was applied to a 350 MW boiler, achieving an error of 2.41%, proving its real-world effectiveness." @default.
• W4386574039 created "2023-09-10" @default.
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• W4386574039 date "2023-11-01" @default.
• W4386574039 modified "2023-10-02" @default.
• W4386574039 title "Efficient Online Prediction and Correction of 3D Combustion Temperature Field in Coal-Fired Boilers using GDNN" @default.
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• W4386574039 doi "https://doi.org/10.1016/j.measurement.2023.113507" @default.
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