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Pilot-scale treatment of atrazine production wastewater by UV/O 3 /ultrasound: Factor effects and system optimization
This study shed light on removing atrazine from pesticide production wastewater using a pilot-scale UV/O /ultrasound flow-through system. A significant quadratic polynomial prediction model with an adjusted R of 0.90 was obtained from central composite design with response surface methodology. The o...
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Published in: | Journal of environmental management 2017-12, Vol.203 (Pt 1), p.182-190 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This study shed light on removing atrazine from pesticide production wastewater using a pilot-scale UV/O
/ultrasound flow-through system. A significant quadratic polynomial prediction model with an adjusted R
of 0.90 was obtained from central composite design with response surface methodology. The optimal atrazine removal rate (97.68%) was obtained at the conditions of 75 W UV power, 10.75 g h
O
flow rate and 142.5 W ultrasound power. A Monte Carlo simulation aided artificial neural networks model was further developed to quantify the importance of O
flow rate (40%), UV power (30%) and ultrasound power (30%). Their individual and interaction effects were also discussed in terms of reaction kinetics. UV and ultrasound could both enhance the decomposition of O
and promote hydroxyl radical (OH·) formation. Nonetheless, the dose of O
was the dominant factor and must be optimized because excess O
can react with OH·, thereby reducing the rate of atrazine degradation. The presence of other organic compounds in the background matrix appreciably inhibited the degradation of atrazine, while the effects of Cl
, CO
and HCO
were comparatively negligible. It was concluded that the optimization of system performance using response surface methodology and neural networks would be beneficial for scaling up the treatment by UV/O
/ultrasound at industrial level. |
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ISSN: | 0301-4797 1095-8630 |
DOI: | 10.1016/j.jenvman.2017.07.027 |