TY - JOUR
T1 - Enhancing wastewater treatment through artificial intelligence
T2 - A comprehensive study on nutrient removal and effluent quality prediction
AU - Inbar, Offir
AU - Avisar, Dror
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/5
Y1 - 2024/5
N2 - With over 80 % of the world's wastewater discharged without treatment and 2 billion people lacking access to adequate sanitation facilities, optimizing sewage treatment processes is crucial. Utilizing a comprehensive 11-year dataset covering 43 parameters from all stages in a full-scale wastewater treatment plant (WWTP) in Israel, we first examined nutrient removal and key wastewater-quality parameters analysis, revealing seasonal and annual trends' effects on influent and effluent water-quality levels. High temperatures during the summer led to a decrease in the concentration of influent BOD, COD, TSS, and NH4, which can be explained by increased biological activity, enhanced sedimentation, and accelerated nitrification. We then introduced a novel method to forecast total phosphorus in effluent by applying various machine and deep learning algorithms, focusing on binary predictions for regulatory compliance. XGBoost achieved the best results with 87 % accuracy and 85 % precision, while random forest exhibited the highest recall at 90 % on the testing set. Consistent and balanced performance in training and testing indicated neither overfitting nor underfitting. Addressing scenarios without secondary total phosphorus monitoring, time-series LSTM models with a look-back period of 2 days achieved the best results with 77 % accuracy, helping prioritize critical input features for precise predictions in resource-constrained WWTPs. The models enable choosing higher recall or precision rates based on regulations/limitations. The overall results indicate that integrating knowledge of the nutrient-removal process with the application of artificial intelligence enhances WWTP monitoring and mitigates the discharge of low-quality effluent.
AB - With over 80 % of the world's wastewater discharged without treatment and 2 billion people lacking access to adequate sanitation facilities, optimizing sewage treatment processes is crucial. Utilizing a comprehensive 11-year dataset covering 43 parameters from all stages in a full-scale wastewater treatment plant (WWTP) in Israel, we first examined nutrient removal and key wastewater-quality parameters analysis, revealing seasonal and annual trends' effects on influent and effluent water-quality levels. High temperatures during the summer led to a decrease in the concentration of influent BOD, COD, TSS, and NH4, which can be explained by increased biological activity, enhanced sedimentation, and accelerated nitrification. We then introduced a novel method to forecast total phosphorus in effluent by applying various machine and deep learning algorithms, focusing on binary predictions for regulatory compliance. XGBoost achieved the best results with 87 % accuracy and 85 % precision, while random forest exhibited the highest recall at 90 % on the testing set. Consistent and balanced performance in training and testing indicated neither overfitting nor underfitting. Addressing scenarios without secondary total phosphorus monitoring, time-series LSTM models with a look-back period of 2 days achieved the best results with 77 % accuracy, helping prioritize critical input features for precise predictions in resource-constrained WWTPs. The models enable choosing higher recall or precision rates based on regulations/limitations. The overall results indicate that integrating knowledge of the nutrient-removal process with the application of artificial intelligence enhances WWTP monitoring and mitigates the discharge of low-quality effluent.
KW - Artificial intelligence
KW - Nutrient removal
KW - Phosphorus
KW - Sewage treatment
KW - Wastewater
UR - http://www.scopus.com/inward/record.url?scp=85189966499&partnerID=8YFLogxK
U2 - 10.1016/j.jwpe.2024.105212
DO - 10.1016/j.jwpe.2024.105212
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AN - SCOPUS:85189966499
SN - 2214-7144
VL - 61
JO - Journal of Water Process Engineering
JF - Journal of Water Process Engineering
M1 - 105212
ER -