Dynamic modeling research on thermal efficiency of supercritical down-fired boiler

To accurately predict the coal-fired generating units? thermal efficiency under deep peak shaving conditions, a dynamic prediction method of boiler thermal efficiency is proposed. Determine the auxiliary variables affecting boiler thermal efficiency by analyzing the proportions of heat loss in the anti-balance method, and using the random forest algorithm to carry out supervised dimensionality reduction of auxiliary variables affecting the thermal efficiency. On this basis, the convolutional neural network (CNN) with dynamic modelling function is selected as the infrastructure, and to ensure the lightweight model, the cross-channel communication unit is inserted into the conventional CNN with only three convolutional layers to solve the problem of no interaction of feature maps in the same convolutional layer, and a boiler thermal efficiency predictive model based on the cross-channel communication CNN (C3-CNN) is constructed. Simulation experiments were conducted on the actual operation data for a 600 MW boiler, the results show that the developed method is equally suitable for both transient and steady-state conditions.