MI-VMD-BSCNet: A lightweight spatiotemporal modeling framework for tube temperature prediction in coal-fired boiler water-walls

Over-temperature of boiler water-walls causes tube leakage in ultra-supercritical coal-fired power units. This is a critical issue intensified by frequent load fluctuations from flexible peak shaving, essential for carbon peaking and neutrality goals. Existing computational fluid dynamics methods have high computational load, limiting their suitability for real-time monitoring, while data-driven approaches cannot accurately capture dynamic temperature changes under rapid load ramp. This study proposes a lightweight spatiotemporal modeling framework, referred to as mutual information-variational mode decomposition-broad skip connection network (MI-VMD-BSCNet), for high-accuracy and low-cost water-wall temperature prediction, advancing artificial intelligence applications in energy systems. A feature selection method reduces the input complexity, advanced signal processing enhances the temporal feature representation, and a sliding window approach captures the underlying local and global patterns. BSCNet leverages a parallel feature extraction architecture and skip connections to optimize feature fusion and gradient flow, allowing to improve the modeling of dynamic temperature variations. The model is trained and evaluated using historical data from a 1000 MW ultra-supercritical coal-fired boiler. The obtained results demonstrate that it outperforms baseline convolutional neural network and broad learning system models, achieving mean absolute error, mean absolute percentage error, and root mean square error of 1.493 °C, 0.395%, and 1.964 °C, respectively. This framework enables early warning of over-temperature failures, which supports sustainable boiler operation and provides a high potential for theoretical and engineering advancements.