Research on heat transfer coefficients of condensers based on CNN-BiGRU-AT

To master the heat transfer performance of the condensers， a model based on convolutional neural network and bidirectional gated recurrent unit with attention mechanism （CNN-BiGRU-AT） is adopted to predict their heat transfer coefficients. Taking the condenser of a 1，000 MW unit in a power plant as the research subject， the condenser characteristics are first analyzed， and a theoretical model for calculating its heat transfer coefficients is constructed. Then， CNN is employed to extract the data features of factors influencing the heat transfer characteristics， while BiGRU is utilized to capture the long and short-term dependencies between the data. Finally， AT is used to highlight the important parts of the features， thereby constructing a prediction model for heat transfer coefficients based on CNN-BiGRU-AT. The analysis of the computational results indicates that compared to traditional theoretical calculations and BP （backpropagation） networks， as well as GRU networks， the computational results obtained using the CNN-BiGRU-AT model are more accurate and exhibit better regularity.