Application of Deep Learning Algorithm in Optimization Control of Electrostatic Precipitator in Coal-Fired Power Plants

Abstract

The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants. The pollution reduction and carbon emissions reduction in coal-fired power plants will be a key task in the future. In this paper, an optimization technique for the operation of an electrostatic precipitator is proposed. Firstly, the voltage-current model is constructed based on the modified dust charging mechanism; the modified parameters are trained through the gradient descent method. Then, the outlet dust concentration prediction model is constructed by coupling the mechanism model with the data model; the data model adopts the long short-term memory network and the attention mechanism. Finally, the particle swarm optimization algorithm is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate predictions of the secondary current and outlet dust concentration of the electrostatic precipitator have been achieved. The mean absolute percentage error of the voltage-current characteristic model is 1.43%, and the relative root mean-squared error is 2%. The mean absolute percentage error of the outlet dust concentration prediction model on the testing set is 5.2%, and the relative root mean-squared error is 6.9%. The optimization experiment is carried out in a 330 MW coal-fired power plant. The results show that the fluctuation of the outlet dust concentration is more stable, and the energy saving is about 43% after optimization; according to the annual operation of 300 days, the annual average carbon reduction is approximately 2621.34 tons. This method is effective and can be applied widely.