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  = 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.