Hybrid modeling for energy consumption prediction of desulfurization wastewater bypass evaporation system

The bypass evaporation system for desulfurization wastewater can reduce the efficiency of the boiler and increase coal consumption by extracting part of the hot flue gas at the inlet of the air preheater. To achieve accurate prediction of the energy consumption caused by the extracted hot flue gas, a hybrid model prediction method combining a mechanistic model and an artificial neural network was proposed. Operational data from a 660MW power plant in Guangdong Province were collected as samples. Six parameters, including the wind temperature at the inlet of the air preheater, the flow rate of air, the extracted flue gas volume and temperature, the boiler load, and the coal feed rate, were used as inputs to establish a backpropagation neural network (BPNN) model for predicting the air heat transfer through the air preheater. The optimal structure of the network was determined by simulating and analyzing different hidden layer structures, and it was found to be 6-9-1, with a coefficient of determination (R2) of 0.99478 and a relative error of about 1% for the prediction model. The overall prediction effect of the model was good. Based on this, the qualitative law of energy consumption for the bypass evaporation system was obtained by combining the mechanistic model and typical operating conditions of fluctuating unit loads and extracted flue gas volumes. © 2024 Chemical Industry Press Co., Ltd.. All rights reserved.