Numerical calculations of submicron particle removal in a spike-plate electrostatic precipitator

Farnoosh, Niloofar; Adamiak, K.; Castle, G.S.P.

doi:10.1109/tdei.2011.6032814

Cited by 43 publications

(8 citation statements)

References 33 publications

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“…The gas flow, electric field, and particle transport inside ESP exert an influence on each other [22][23][24][25]. The interaction between them is shown in Figure 1.…”

Section: Mathematical Modelmentioning

confidence: 99%

The Influence of Electrohydrodynamic Secondary Flow on the Collection Efficiency and Deposition Pattern in ESP

Guo

Wang

et al. 2019

Mathematical Problems in Engineering

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Electrostatic precipitator (ESP) is widely used for dust removal from flue gas in industry. In the electrostatic precipitation, the electrohydrodynamic secondary flow (EHD) produced by corona ionization has an important influence on the characteristics of particle transport and the collection efficiency of ESP. In this work, a comprehensive ESP model with interaction of multiple physical fields is established to study the EHD effect in ESP. The numerical results show that the EHD generally can increase the streamwise velocity of airflow near the collection plate, which makes the removal performance of ESP worsen. Meanwhile, the EHD has a significant effect on the particle deposition pattern, especially at lower flue gas velocity. When the needle tip of discharge electrode points to the collection plate, the EHD can promote the circulation of airflow near the corona wire, increase the probability of particle charging, and then improve the collection efficiency of ESP.

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“…The gas flow, electric field, and particle transport inside ESP exert an influence on each other [22][23][24][25]. The interaction between them is shown in Figure 1.…”

Section: Mathematical Modelmentioning

confidence: 99%

The Influence of Electrohydrodynamic Secondary Flow on the Collection Efficiency and Deposition Pattern in ESP

Guo

Wang

et al. 2019

Mathematical Problems in Engineering

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“…In the filamentary ion flow field model, the atmosphere ion mobility is a crucial physical parameter, which refers to an average mobility for the various ionic species in the ion drift region. The atmospheric ion mobility is usually considered as a constant proportionality coefficient in the realm of electrohydrodynamics and electromagnetism [6][7][8]. In fact, the mobility is a multivariable function and determined by many impacts, including the atmospheric composition, temperature, humidity, atmospheric pressure, atmospheric pollutants, etc.…”

Section: Introductionmentioning

confidence: 99%

“…The temporal decay of the current and the ion drift time were monitored during the test [11][12]. However, in the earlier studies, the measurement of ion mobility had mainly focused on SF 6 or ion mobility had been measured at low temperature. Ion mobility spectrometry was the most widely used approach to measure the ion mobility in the atmosphere; meanwhile, the ironic species could be identified [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assisted Prediction Algorithm of Atmospheric Ion Mobility

Chen

Wang

2020

IEEE Access

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Atmosphere ion mobility is an important electrical parameter related to the filamentary ion flow field of high voltage direct current (HVDC) transmission lines and other characteristics of the glow and streamer corona discharge. Many machine learning (ML)-based algorithms have already been widely used in the prediction of air discharge. This paper proposes a genetic algorithm (GA)-support vector regression (SVR) combined with kernel principal components analysis (PCA) to predict the ion mobility, involving dimensionality reduction, feature selection, parameter optimization of SVR. Kernel PCA could reduce the dimension of data and GA with adaptive probability parameters is employed to optimize the parameters of SVR model. An improved parallel-plates ion generator is employed to produce corona discharge and then measure the saturation ion current density and then obtain the training data and testing data of ion mobility. The prediction results show that the proposed algorithm outperforms the other methods in terms of mean relative error and mean squared error criteria. In addition, the parameters of model and data features have a major influence on the performance of the prediction algorithm. Based on the measured data and reference data, the prediction result obtained on ion mobility under different humidity shows a satisfactory generalization and effectiveness of proposed model for the prediction.

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“…23,30 The electric power consumption of the ESP with spikes on both sides is nearly twice as much as that of the ESP with spikes on only one side. 31 The electrical characteristics and ionic wind vary with the spike point spacing. 32 A numerical model of ESP has been developed to describe the electric eld, and the rotation of the discharge electrode optimizes its electric eld.…”

Section: Introductionmentioning

confidence: 99%

Current density distribution and optimization of the collection electrodes of a honeycomb wet electrostatic precipitator

et al. 2018

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Numerical calculations of submicron particle removal in a spike-plate electrostatic precipitator

Cited by 43 publications

References 33 publications

The Influence of Electrohydrodynamic Secondary Flow on the Collection Efficiency and Deposition Pattern in ESP

The Influence of Electrohydrodynamic Secondary Flow on the Collection Efficiency and Deposition Pattern in ESP

Machine Learning Assisted Prediction Algorithm of Atmospheric Ion Mobility

Current density distribution and optimization of the collection electrodes of a honeycomb wet electrostatic precipitator

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