期刊
ENVIRONMENTAL RESEARCH
卷 210, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.envres.2022.112953
关键词
Soft sensor; Machine learning; XGBoost; Real-time monitoring; Biochemical oxygen demand (BOD)
资金
- Drainage Services Department of Hong Kong
- Hong Kong Innovation and Technology Commission [ITC-CNERC14EG03]
- Hong Kong Research Grant Council [T21-604/19-R]
- Ghent University [BOF/STA/202109/022]
This study developed a new soft sensor using XGBoost machine learning to measure the concentration of organics in wastewater. Compared to conventional soft sensors, this new sensor can more accurately detect extremely high levels of pollutants.
In wastewater monitoring, detecting extremely high pollutant concentrations is necessary to properly calibrate the treatment process. However, existing hardware sensors have a limited linear range which may fail to measure extremely high levels of pollutants; and likewise, the conventional soft model sensors are not suitable for the highly-skewed data distributions either. This study developed a new soft sensor by using eXtreme Gradient Boosting (XGBoost) machine learning to 'measure' the wastewater organics (in terms of 5-day biochemical ox-ygen demand (BOD5)). The soft sensor was tested on influent and effluent BOD5 of two different wastewater treatment plants to validate the results. The model results showed that XGBoost can detect these extreme values better than conventional soft sensors. This new soft sensor can function using a sparse input matrix via XGBoost's sparsity awareness algorithm -which can address the limitation of the conventional soft sensor with the fallibility of supporting hardware sensors even.
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