Prediction and Comparative Analysis of the Influence of Magnetic Field Effect on PM2.5 Trapping Efficiency in Electrostatic Precipitator (ESP) under Different Temperatures

Abstract

Charged particles have high momentum under high-temperature conditions, which helps to promote their movement towards a dust collector in a magnetic field environment, making it possible to improve the efficiency of the high-temperature wire-plate electrostatic precipitator (ESP) in this environment. A multi-field coupling model was established to numerically simulate PM2.5 dust-removal efficiency in an ESP under different working conditions. Combining the particle swarm optimization (PSO) algorithm with the support vector machine (SVM) model, the PSO-SVM prediction model is presented. Simulated data were used as training data, and PSO-SVM and back-propagation (BP) neural network models were utilized to predict collection efficiency under different working conditions, respectively. The results show that introducing a magnetic field can effectively improve the PM2.5 collection efficiency of wire-plate ESP, and the effect of a magnetic field on the dust-removal efficiency is more obvious at higher temperatures and higher flue gas velocities. When changing the working conditions, the predicted results of the magnetic field effect conform to simulated ones, and the PSO-SVM predicted values have a smaller relative error than those of the BP model, which can better adapt to different working conditions. All of the above conclusions can be utilized as a simple and adequately efficient example of the ESP model for follow-up research.