Shiming Xu

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.

To reduce coal consumption, nitrogen oxide (NOx), and carbon emissions for coal-fired units, combustion optimization has become not only a hot issue for scientists but also a practical engineering for engineers. A data-driven multiple linear regression (MLR) model is proposed to solve the time-consuming problems of boiler online combustion optimization systems. Firstly, A whole year’s worth of the historical operating data preprocessing procedure of a coal-fired boiler in a power station including data resampling, data cleaning, steady-state selection, and cluster analysis is performed. In order to meet the applicable conditions of the linear model, the historical operating data are divided into different sub-datasets (combination mode of coal mills, main steam flow, ambient temperature, lower heating value of coal). Secondly, the multi-objective optimization strategy of economical, carbon, and NOx emissions indexes is employed to select operating optimum data packets, and a new dataset is established that is better than the average value of the optimization target in each sub-dataset. On this basis, a stepwise regression algorithm (SRA) is used to select the specific manipulated variables (MVs) that are significant to the multiple optimization targets from 47 candidate MVs in each sub-dataset (different partitions have different types of MVs), and an MLR prediction model is developed. In order to further realize combustion optimization control, the MVs are optimized by employing the MLR model. According to the deviation between the optimal value and the real-time value of the MVs, a boiler combustion closed-loop control system is developed, which is connected with the DCS using the sum of the deviation signal and the corresponding original one. Then, a boiler combustion application test was carried out under some working conditions to verify the feasibility and effectiveness of the approach. The update time of the system signals running on industrial computers is less than 1 s and suitable for online applications. Finally, a full-scale test of the combustion optimization online control system (OCS) is executed. The results show that the boiler thermal efficiency increased by 0.39% based on standard coal, the NOx emissions reduced by 2.85% and the decarbonization effect is significant.