Abstract:

The design of the SCR denitrification system in coal-fired power plants requires the efficient denitrifi-cation efficiency and the low cost. Hence, it is essential to accurately calculate the volume of SCR denitrification catalysts. The flue gas conditions of thermal power plants are complex and changeable. Flue gas temperature, flue gas flow, inlet and outlet NO_x concentrations, and other parameters all affect the volume of the SCR catalyst.Therefore, catalyst volume prediction is a multifactor coupling problem. For this feature, the BP neural network model was used to predict the volume design of the catalyst, and the neural network modeling based on genetic algorithm optimization was investigated for the structural defects of the BP neural network model. The results show that the prediction accuracy of BP neural network model optimized by genetic algorithm is promising, which provides a new way for catalyst volume design of SCR denitrification.

Key words: SCR catalyst, catalyst volume prediction, BP neural network, genetic algorithm