| 학회 |
한국공업화학회 |
| 학술대회 |
2020년 가을 (10/28 ~ 10/30, 광주 김대중컨벤션센터(Kimdaejung Convention Center)) |
| 권호 |
24권 1호 |
| 발표분야 |
포스터-환경·에너지 |
| 제목 |
Prediction of Oxidant Exposures and Micropollutant Abatement during Ozonation Using a Machine Learning Method |
| 초록 |
The oxidation of micropollutants (MPs) by ozonation proceeds via the reactions with molecular ozone (O3) and hydroxyl radical (•OH). To predict MP abatement during ozonation, developing a model that can accurately predict oxidant exposures needs to be preceded. This study demonstrates machine learning models based on the Random Forest (RF) algorithm to output oxidant exposures from water quality parameters including fluorescence excitation-emission matrix (FEEM) data for organic matter characterization. Oxidant exposure of sixty natural waters and wastewater effluents samples were determined. Models using high resolution FEEM data generally exhibited high prediction accuracy, implying that the organic matter characteristics quantified by FEEM can be important factors to improve the accuracy of the prediction model. |
| 저자 |
차동원1, 박상훈2, 김민식1, 김태완1, 홍석원3, 조경화2, 이창하1
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| 소속 |
1서울대, 2울산과학기술원, 3KIST |
| 키워드 |
Ozonation; Oxidant exposure; Modeling; Machine learning; FEEM
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| E-Mail |
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