电除尘器
微粒
平方(代数)
阶段(地层学)
单级
章节(排版)
横截面(物理)
材料科学
工程类
化学
物理
电气工程
航空航天工程
几何学
数学
计算机科学
地质学
古生物学
有机化学
量子力学
操作系统
作者
Aiswarya Kumar,Lazarus Godson Asirvatham,Manoranjan Sahu,Y.S. Mayya
标识
DOI:10.1080/10962247.2025.2467666
摘要
Electrostatic precipitation (ESP) is a technology widely used to remove particulate matter (PM) from industrial gas streams. To adopt the same for varying scales as well as for different clean air delivery applications as in indoor and outdoor air pollution, there exists a requirement for the development of comprehensive, readily adaptable, reasonably good, comparable, rigorous, step-by-step analytical theory and experimental validation of same for design of modular units of ESP. In this regard, the current study conducted theoretical and experimental studies to investigate corona characterization and PM collection efficiency in a modular unit of a single-wire, single-stage, wire-plate ESP with square cross-sectional geometry. The best agreement between the I-V characteristics of theory and the experiment was obtained while adjusting the inception electric field to 12.35 × 105 Vm−1 as well as the ion diffusion coefficient value to to 0.0647 × 10−4 m2s−1.Tuned theory predicted PM collection efficiency at three different flow rates of 30, 50 as well as 100 LPM and at various potentials 9 kV, 11 kV as well as 13 kV respectively. Comparing the predicted results from theory and experiment, it is understood that agreement between theory and experiment is acceptable in the case of varied flow rates and is good for potentials for varied size ranges from 13 nm to 800 nm. As accuracy and reliability of present model are verified in terms of collecting efficiency at different operating conditions of flow rate as well as potential, present model can facilitate the design and scale-up of ESPs for indoor PM control with high collection efficiency. The study also illustrated a sample calculation on the applicability of this filter-less technology for air cleaning in an indoor environment.
科研通智能强力驱动
Strongly Powered by AbleSci AI