Data-Driven High-Temperature Superheater Wall Temperature Prediction Using Polar Lights Optimized Kolmogorov–Arnold Networks

The flexible operation of coal-fired boilers poses significant challenges to thermal safety, particularly due to delayed responses in wall temperature under variable load conditions, which may lead to overheating risks and reduced equipment lifespan. To address this issue, we propose a PLO-KAN framework for high-precision prediction of high-temperature superheater wall temperatures. The framework integrates a Kolmogorov–Arnold Network (KAN) with learnable B-spline activation functions to enhance interpretability, a sliding-window strategy to capture temporal dependencies, and Polar Lights Optimization (PLO) for automated hyperparameter tuning, balancing local exploitation and global exploration. The method is validated using 10,000 operational samples from a 1000 MW ultra-supercritical once-through boiler, with 68 key features selected from 106 candidates. Results show that the proposed model achieves high accuracy and robustness in both single-step and multi-step forecasting, maintaining reliable performance within a five-minute prediction horizon. The proposed method provides an efficient and interpretable solution for real-time wall temperature prediction, supporting proactive thermal management and enhancing operational safety in coal-fired power plants.