| Abstract |
| Despite the extensive data collection facilitated by water quality sensors in wastewater treatment plants (WWTPs), certain parameters such as chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) still require analysis through time-consuming experimentation. Moreover, the complex interactions between biological and physicochemical treatment processes, coupled with fluctuations in influent quality and quantity, lead to significant variations in operational conditions and treatment efficiencies in WWTPs, presenting challenges for monitoring, operation, and maintenance. Consequently, conventional modeling methodologies may prove insufficient for practical operational environments. This study developed novel ensemble tree-based models to enhance real-time predictions of COD and TN concentrations, which are difficult to monitor directly. In addition, a novel approach was proposed to assess anaerobic-anoxic-oxic (A₂O) process performance by analyzing the correlation between the predicted carbon/nitrogen (C/N) ratio and the removal efficiencies of COD and TN. During a one and a half year monitoring period, the predicted C/N ratio accurately reflected changes in COD and TN removal efficiencies across the different A₂O bioreactors. The results provide real-time COD and TN predictions and a method for assessing A₂O process performance based on the C/N ratio, which can significantly aid in the operation and maintenance of biological wastewater treatment processes. |
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| Key Words |
| A₂O공정, 화학적산소요구량, 총질소, 수질예측, A₂O process, COD, TN, Water quality prediction |
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