Dynamic Correlation-Guided Graph Spatiotemporal Learning for Bed Temperature Prediction of Circulating Fluidized Beds

Accurate bed temperature prediction is crucial in the circulating fluidized bed (CFB) combustion process, as it provides early warning of abnormal conditions, allowing timely intervention to prevent potential safety risks. However, the inherently complex multiphase flows and nonlinear chemical reactions in CFB systems make time series prediction of bed temperature a highly challenging task. Starting from the intrinsic spatial correlations and the temporal dependencies, this article proposes an adaptive learning-based bed temperature prediction method. The proposed model integrates dynamic correlation-guided Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) networks. Specifically, the GCN guided by prior knowledge adaptively learns complex topological structures to capture spatial dependencies. LSTM receives both raw input features and the spatial features extracted by GCN as parallel inputs, effectively capturing the temporal evolution of bed temperature. The proposed method is then applied to the task of bed temperature prediction in CFB. The experimental findings indicate that the proposed method consistently outperforms other comparative methods across different forecasting horizons, achieving a leading level of performance.