TY - JOUR
T1 - Novel Intelligent Control Framework for WWTP Optimization to Achieve Stable and Sustainable Operation
AU - Feng, Kuanliang
AU - Zhao, Zihao
AU - Li, Mengyan
AU - Tian, Luling
AU - An, Tong
AU - Zhang, Jiawei
AU - Xu, Xiangyang
AU - Zhu, Liang
N1 - Funding Information:
This work was supported by the Major Scientific and Technological Project of Zhejiang Province (2021C03021), the Major Scientific and Technological Project of Zhejiang Province (2022C03075), and National Natural Science Foundation of China (51961125101). Mengyan Li was supported by the National Science Foundation under Grant (No. 1903597).
Publisher Copyright:
© 2012 American Chemical Society. All rights reserved.
PY - 2022/11/11
Y1 - 2022/11/11
N2 - Intelligent control is a promising approach to achieve stable and sustainable operation at municipal wastewater treatment plants (WWTPs). A desirable WWTP intelligent control system can be responsive to influent dynamics and adaptable for complex multi-objective optimization. In this study, we developed a novel intelligent control framework based on machine learning methods, which comprises a prediction module and control module. The stacking ensemble learning model (SELM) and Q-learning model (QLM) were used to capture influent dynamics and intelligently identify optimal parameters, respectively. This SELM-QLM framework was trained and validated with historical monitoring data archived at a full-scale WWTP to optimize the nitrogen removal process. The results showed that control parameters were frequently adjusted in response to influent variation and energy consumption of aeration, and the sludge returning process was effectively decreased while maintaining the stability of effluent total nitrogen (TN) (TN decreased by 19.53% and energy consumption decreased by 10.37%). Specifically, the SELM provided accurate predictions of TN concentration without increasing the data set scale, and the QLM showed superior ability in determining the optimal solution from nearly contradictory objectives. This study provides a framework with significant application values for improving WWTP management inspired by the objective of stable and sustainable operation.
AB - Intelligent control is a promising approach to achieve stable and sustainable operation at municipal wastewater treatment plants (WWTPs). A desirable WWTP intelligent control system can be responsive to influent dynamics and adaptable for complex multi-objective optimization. In this study, we developed a novel intelligent control framework based on machine learning methods, which comprises a prediction module and control module. The stacking ensemble learning model (SELM) and Q-learning model (QLM) were used to capture influent dynamics and intelligently identify optimal parameters, respectively. This SELM-QLM framework was trained and validated with historical monitoring data archived at a full-scale WWTP to optimize the nitrogen removal process. The results showed that control parameters were frequently adjusted in response to influent variation and energy consumption of aeration, and the sludge returning process was effectively decreased while maintaining the stability of effluent total nitrogen (TN) (TN decreased by 19.53% and energy consumption decreased by 10.37%). Specifically, the SELM provided accurate predictions of TN concentration without increasing the data set scale, and the QLM showed superior ability in determining the optimal solution from nearly contradictory objectives. This study provides a framework with significant application values for improving WWTP management inspired by the objective of stable and sustainable operation.
KW - Q-learning
KW - Shapley additive explanations (SHAP)
KW - municipal wastewater treatment plant
KW - stacking ensemble learning
KW - total nitrogen
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U2 - 10.1021/acsestengg.2c00156
DO - 10.1021/acsestengg.2c00156
M3 - Article
AN - SCOPUS:85141994348
SN - 2690-0645
VL - 2
SP - 2086
EP - 2094
JO - ACS ES and T Engineering
JF - ACS ES and T Engineering
IS - 11
ER -