Outlet Dust Concentration Prediction of Electrostatic Precipitator Based on Deep Learning Algorithm

Electrostatic precipitator is the main equipment for removing dust in coal-fired power plants, it removes dust by consuming electrical energy. Establishing an accurate prediction model for electrostatic precipitator can provide important scientific basis for reducing pollution and carbon emissions in coal-fired power plants, which is of great significance for achieving the dual-carbon target. This paper proposes a mechanism model, and the variable parameters are trained through Particle Swarm Optimization algorithm. Then a hybrid model is established based on long short-term memory network and Attention mechanism. The historical data of electrostatic precipitator in a 330MW unit are used for training and testing finally. The results show that the Relative Root Mean Squared Error of hybrid model on the training set and testing set are 0.5% and 3.99%, accurate outlet dust concentration prediction has been realized.