融合物理先验的PID-CTNet锅炉状态协同预测OA

[Objective]Coal-fired power units typically exhibit strong nonlinearity,long time delays,and pronounced physical couplings during operation.These characteristics make it difficult for traditional mechanism-based or purely data-driven modeling approaches to simultaneously achieve high dynamic accuracy,strong generalization capability,and low deployment cost in real industrial environments.To address these challenges,this study proposes a physics-informed dual-branch convolutional transformer network(PID-CTNet)that integrates physical priors with deep learning to achieve collaborative prediction of multiple combustion-related variables.[Methods]First,a multi-source feature space is reconstructed based on combustion and thermodynamic mechanisms.Twenty-seven key physical variables are selected from four categories(load commands,fuel and air-gas system parameters,thermodynamic properties of the working fluid,and historical feedback indicators)to form a physically meaningful input representation.Second,a multi-task deep learning framework combining convolutional neural networks(CNN)and Transformer is developed.The front-end temporal convolutional networks(TCN)extract local transient features and perform dimensionality reduction,while the back-end Transformer introduces a self-attention mechanism with a global receptive field to capture long-term dependencies and large-delay characteristics inherent in boiler dynamics.Finally,a hard parameter-sharing strategy is employed to enable simultaneous prediction of NOx,SO2,and CO emissions,as well as main and reheat steam temperatures,within a unified modeling framework.[Results]Experimental results demonstrate that the proposed end-to-end model achieves high prediction accuracy,with root mean square error of 2.895,mean absolute error of 1.654,mean absolute percentage error of 0.565%,and coefficient of determination R2 of 0.957 on the test set.The prediction errors of all five industrial key indicators remained below 3%,significantly outperforming traditional regression and time-series models such as SVR and LSTM,as well as single-task deep learning baselines.Ablation studies further validate the effectiveness of incorporating physical priors,the complementary roles of the TCN-Transformer hybrid architecture,and the performance gains brought by the multi-task learning mechanism.These components jointly enhance model accuracy,stability,and robustness under complex operating conditions,including load fluctuations,fuel variations,and frequent operational adjustments.[Conclusion]The PID-CTNet model effectively integrates physical mechanisms with deep learning,achieving a favorable balance between prediction accuracy and computational efficiency.By providing lightweight and reliable collaborative prediction of emissions and steam temperature parameters,the proposed method offers a practical and deployable solution for intelligent operation,emission optimization,and safety control of coal-fired power units.The results demonstrate that physics-informed multi-task learning is a promising direction for improving the interpretability,engineering applicability,and long-term maintainability of data-driven models in modern power plant operation and optimization.