Electrostatic precipitators in the era of smart technology: Electrical efficiency, vibration mitigation, and noise reduction

Abstract

Abstract

Electrostatic precipitators (ESPs) are significant devices for particulate control in power generation, cement production, steel manufacturing, and other high-emission sectors. Future ESP performance should be evaluated not only on particle collection efficiency but also on electrical energy consumption, vibration stability, noise levels, reliability, and adaptive functionality. We proposed a three-layered framework for perception, analysis, and execution in intelligent ESPs. The perception layer combines electrical, emission, process, vibration, acoustic, and maintenance data; the analysis layer applies signal processing, artificial intelligence, digital twins, and multi-objective optimization; and the execution layer provides adaptive voltage control, rapping optimization, fan-speed control, vibration mitigation, active noise control, and predictive maintenance. The quantitative data show that intelligent electrical optimization can reduce ESP energy consumption by 35.50% and improve emission compliance from 95% to 100%. Approximately 43% energy saving was achieved by deep-learning-assisted voltage optimization in a 330 MW coal-fired power plant ESP. FFT, wavelet transform, RMS tracking, CNNs, LSTM models, and autoencoders can be used as diagnostic methods to detect imbalance, resonance, bearing faults, and fan irregularities, thereby reducing vibrations. Hybrid passive-active control for noise reduction can attenuate low-frequency duct noise by more than 10 dB and high-frequency components by more than 20 dB. This review highlights the absence of field-validated collaborative optimization as a significant knowledge gap and suggests digital twins, edge computing, 5G/6G communication, and multi-objective control as promising avenues for future research.