A deep graph convolutional network model of NOx emission prediction for coal-fired boiler

To deal with environmental problems caused by NOx production in thermal plants, it is imperative to establish a reliable model to predict NOx concentration in the combustion process. NOx formation in a coal-fired boiler is complex, and many variables affect NOx emissions. The effective information fusion of these variables can improve the accuracy of NOx concentration prediction. However, the existing NOx prediction algorithms based on thermal parameters rarely consider the mechanical knowledge of the boiler operation, and it is not easy to incorporate the topological information of production into modelling. Therefore, a graph convolutional network is proposed for NOx emission prediction. First, the key variables affecting NOx generation are selected according to the knowledge and the random forest-based variable importance. Then, the model structure is designed by exploring the topological information among thermal variables to capture the complex spatial dependence. The model inputs are constructed by coding different operation variables, and the adjacency matrix is generated according to the correlation information between variables, which can fuse data information and reduce redundancy. On this basis, the prediction model of NOx concentration is established. Historical data from a 660 MW coal-fired boiler are used in the experiment. The prediction results show that the proposed model can effectively fuse the information of characteristic variables and fully exploit the non-linear mapping relationship between process variables and NOx emission. When compared with three typical models in NOx modelling, the proposed model has better performance with a determination coefficient of 0.906.