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Degradation of Octocrylene Using Combined Ozonation and Electrolysis Process: Optimization by Response Surface Methodology
Author(s) -
Wu Donghai,
Zhang Ran,
Lu Guanghua,
Lin Qiuhong,
Liu Fuli,
Li Yi
Publication year - 2017
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.201500664
Subject(s) - response surface methodology , electrolysis , ozone , chemistry , aqueous solution , degradation (telecommunications) , cathode , central composite design , process optimization , chemical engineering , chromatography , electrode , organic chemistry , electrolyte , telecommunications , computer science , engineering
This work investigated the use of combined ozonation and electrolysis (ozone‐electrolysis) process for octocrylene (OC) removal from aqueous solution. Degradation kinetics and mechanism were investigated, and the operational parameters were optimized employing response surface methodology (RSM). Compared to sole ozonation or electrolysis, the combined ozone‐electrolysis tests yield synergistic degradation of OC ( e.g ., 95.1% removal within 10 min) and reduced the energy consumption ( e.g ., for 90% OC removal, energy requirement was reduced by 29–40%), which may be due to the enhanced production of hydroxyl radicals. The RSM analysis indicated that appropriate increase in the inlet ozone concentration (10–30 mg/L) and initial solution pH (4–10) could enhance the treatment efficiency, while the effect of current density (5–35 mA/cm 2 ) at the cathode was complex. The optimum parameters were determined as the inlet ozone concentration of 26.1 mg/L, cathodic current density of 26.4 mA/cm 2 , and initial pH of 10. Under these conditions, 87.2% OC removal was experimentally obtained at 5 min treatment, which fitted the model prediction well. These observations suggest that the ozone‐electrolysis process is efficient for OC degradation in aqueous solution, and RSM is suitable for process optimization.