Publications by authors named "Yadan Yu"
Modeling the pollutant removal performance of wastewater treatment plants (WWTPs) plays a crucial role in regulating their operation, mitigating effluent anomalies and reducing operating costs. Pollutants removal in WWTPs is closely related to microbial activity. However, there is extremely limited knowledge on the models accurately characterizing pollutants removal performance by microbial activity indicators.
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- The Sustainable Development Goals aim to reduce pollutants and carbon dioxide, positioning microalgae-based wastewater treatment (MBWT) as a promising solution that removes pollutants while converting CO2 into biomass.
- The performance of MBWT systems heavily depends on light conditions, with factors like wavelength, intensity, and photoperiod influencing biomass production, pollutant removal, and metabolite generation.
- To optimize MBWT systems, strategies for photobioreactor design must consider light dynamics and address current research gaps focusing on enhancing both microalgal strains and reactor functionality.
Wastewater treatment based on the activated sludge process is complex process, which is easily affected by influent quality, aeration time and other factors, leading to unstable effluent. Facing increasingly stringent sewage discharge standards in China, it is necessary to build a prediction model for early warning of effluent quality. In this study, nine machine learning algorithms were adopted to construct models for the prediction of effluent Chemical Oxygen Demand (COD).
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