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Open AccessArticle

Optimization of Horseradish Peroxidase Catalytic Degradation for 2-Methyl-6-Ethylaniline Removal Using Response Surface Methodology

1
School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China
2
Tianfu College of Southwestern University of Finance and Economics, Mianyang 621000, China
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Authors to whom correspondence should be addressed.
Water 2019, 11(5), 1093; https://doi.org/10.3390/w11051093
Received: 8 May 2019 / Revised: 21 May 2019 / Accepted: 22 May 2019 / Published: 24 May 2019
(This article belongs to the Section Water and Wastewater Treatment)
For optimizing the reaction conditions of 2-methyl-6-ethylaniline (MEA) degradation catalyzed by horseradish peroxidase (HRP), a response surface methodology with three factors and three levels was used in this research to establish a regression model, a ternary quadratic polynomial, in order to analyze temperature, H2O2 concentration and pH effects on MEA removal efficiency. The results showed that the regression model was significant (p < 0.0001), fitted well with experimental data and had a high degree of reliability and accuracy, and the data were reasonable with low errors. By analyzing interactions and solving the regression model, the maximum MEA removal efficiency was 97.90%, and the optimal conditions were defined as follows: pH 5.02, H2O2 concentration 13.41mM, and temperature 30.95 °C. Under the optimal conditions, the average MEA removal efficiency obtained from the experiments was 97.56%. This research can provide reference for the treatment of actual acetochlor industrial wastewater. View Full-Text
Keywords: horseradish peroxidase; 2-methyl-6-ethylaniline; removal efficiency; response surface methodology horseradish peroxidase; 2-methyl-6-ethylaniline; removal efficiency; response surface methodology
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Shen, S.; Wang, Q.; Shu, J.; Ma, L.; Chen, L.; Xu, Y. Optimization of Horseradish Peroxidase Catalytic Degradation for 2-Methyl-6-Ethylaniline Removal Using Response Surface Methodology. Water 2019, 11, 1093.

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