Deep Learning-Based Surface Temperature Prediction for a Porous Radiant Burner Using Thermocouple-Calibrated Thermal Infrared Images

This paper presents an artificial intelligence (AI)-based method for surface temperature prediction, applying a hybrid convolutional neural network and LASSO regression (CNN-LASSO) to thermocouple-calibrated thermal infrared (IR) images of a large cylindrical top-dome hollow porous radiant burner for the first time. Three CNN architectures, VGG-16, ResNet-50, and DenseNet-121, are evaluated as feature extractors within this framework. Applying the lean premixed CH4/air mixture at a fixed equivalent ratio $\varphi = 0.65$ , the burner is operated at an internal combustion mode over a range of power levels varying from 5 kW to 12 kW controlled by various flow rates of the pre-mixture. The corresponding surface temperature distributions of the porous radiant burner are recorded by an IR camcorder, calibrated by three K-type thermocouples positioned at the top-dome, middle, and bottom of the cylindrical hollow porous burner. The temperature measurement range and the emissivity of the IR camcorder are set at 300-2000°C and 0.95, respectively, having an average temperature of about 40°C difference/uncertainty as compared to that measured by the thermocouples. We evaluate the three CNN-LASSO hybrid models using 900 IR images with 9-fold cross-validation for training and the remaining 100 images for testing. All models exhibit stable and high prediction performance, achieving R2 values above 0.85 on the test set, thereby confirming the effectiveness and robustness of the proposed framework. Thus, the proposed AI-driven framework introduces an innovative and accurate approach to non-intrusive surface temperature measurements using infrared imaging.