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Novel Ensemble Learning Approach for Predicting COD and TN: Model Development and Implementation

Authors :
Qiangqiang Cheng
Ji-Yeon Kim
Yu Wang
Xianghao Ren
Yingjie Guo
Jeong-Hyun Park
Sung-Gwan Park
Sang-Youp Lee
Guili Zheng
Yawei Wang
Young-Jae Lee
Moon-Hyun Hwang
Source :
Water, Vol 16, Iss 11, p 1561 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Wastewater treatment plants (WWTPs) generate useful data, but effectively utilizing these data remains a challenge. This study developed novel ensemble tree-based models to enhance real-time predictions of chemical oxygen demand (COD) and total nitrogen (TN) concentrations, which are difficult to monitor directly. The effectiveness of these models, particularly the Voting Regressor, was demonstrated by achieving excellent predictive performance even with the small, volatile, and interconnected datasets typical of WWTP scenarios. By utilizing real-time sensor data from the anaerobic–anoxic–oxic (A2O) process, the model successfully predicted COD concentrations with an R2 of 0.7722 and TN concentrations with an R2 of 0.9282. In addition, a novel approach was proposed to assess A2O process performance by analyzing the correlation between the predicted 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.

Details

Language :
English
ISSN :
20734441
Volume :
16
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Water
Publication Type :
Academic Journal
Accession number :
edsdoj.7677404733c439280779cd415d98dd6
Document Type :
article
Full Text :
https://doi.org/10.3390/w16111561