Degradation of toluene by tube-tube coaxial dielectric barrier discharge: power characteristics and power factor optimization

Jingwen Wang, Shiye Cheng, Ning Liu, Na Lu, Kefeng Shang, Nan Jiang, Jie Li, Yan Wu

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, the power characteristics and power factor optimization were investigated in a coaxial tube–tube dielectric barrier discharge (DBD) reactor. The effects of several parameters, including discharge voltage, discharge length, discharge frequency and gas flow rate on discharge power and power factor have been evaluated. The experiment results showed that higher discharge power can be obtained by increasing the discharge voltage, discharge frequency and electrode length. But for the power factor, with the increase of discharge frequency, the power factor increased firstly and then decreased. Moreover, with the discharge length increased, the discharge frequency when the power factor reached the maximum value reduced. The response surface method (RSM) and artificial neural network (ANN) were used to optimize the power factor, and their results were relatively consistent. The result of the ANN showed that when discharge voltage was 9.58 kV, discharge frequency was 8.69 kHz, discharge length was 15.8 cm, and gas flow rate was 1.5 L/min, the power factor reached the maximum value of 0.362. The degradation experiment of toluene was carried out in the reactor and its degradation effect was analyzed. The toluene degradation rate is positively correlated with the power factor, and the discharge voltage, gas flow rate and initial concentration are also the key parameters to determine the degradation of toluene. When the discharge voltage, gas flow rate, and initial concentration are 10 kV, 70 mL/min, and 50 ppm, respectively, the power factor and toluene degradation rate reach 0.34 and 74.3%.

Original languageEnglish
Pages (from-to)897-910
Number of pages14
JournalEnvironmental Technology (United Kingdom)
Volume44
Issue number7
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • artificial neural networks (ANN)
  • DBD
  • discharge power
  • power factor
  • response surface methodology (RSM)

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