Modified reconstruction of boiler numerical temperature field based on flame image feature extraction

Computational fluid dynamics (CFD) method is a common method to obtain the temperature field distribution within the furnace. However, in order to reduce computational complexity, the numerical simulation involve simplifications in boundary conditions and model parameters. As a result, the temperature field distribution deviates from the actual operating conditions. This paper proposes a numerical temperature field modification method based on flame images. Flame images corresponding to the operational conditions are collected using the Industrial Flame Monitoring System (IFMS). The flame images are preprocessed, and the contour of the flame's core region is extracted using the Mask Region-based Convolutional Neural Network (Mask R-CNN) method. The geometric features of the flame are extracted, and matrix calculations are applied to modify the numerical temperature field. The modified temperature field exhibits a deviation in the combustion center, aligning with the actual operation of the boiler. A comparison between the modified temperature field and the temperature measurements from flue gas shows consistent temperature trends. The absolute error (AE) under different operating conditions is under 8.8 K, and the relative error (RE) remains below 1.3%. The analysis results demonstrate that it can enhance the accuracy of temperature field calculations by the modification from flame image features.