Hybrid model composed of machine learning and ASM3 predicts performance of industrial wastewater treatment

Xu, Boyan; Pooi, Ching Kwek; Yeap, Tsuey Shan; Leong, Kwok Yii; Soh, Xi Yee; Huang, Shujuan; Shi, Xueqing; Mannina, Giorgio; Ng, How Yong

doi:10.1016/j.jwpe.2024.105888

Journal of Water Process Engineering

2024

DOI: 10.1016/j.jwpe.2024.105888

|View full text |Cite

Hybrid model composed of machine learning and ASM3 predicts performance of industrial wastewater treatment

Boyan Xu,

Ching Kwek Pooi,

Tsuey Shan Yeap

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Industrial activated sludge model identification using hyperparameter-tuned metaheuristics

Nair,

Arivazhagan

2024

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

Industrial activated sludge model identification using hyperparameter-tuned metaheuristics

Nair,

Arivazhagan

2024

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

Improving Prediction of Nutrient Recovery via Struvite Precipitation from Organic Waste Digestate

Aldaach,

Zaki,

Orner

2024

Environmental Engineering Science

View full text Add to dashboard Cite

Machine Learning Methods for the Prediction of Wastewater Treatment Efficiency and Anomaly Classification with Lack of Historical Data

Gulshin,

Kuzina

2024

Applied Sciences

View full text Add to dashboard Cite

This study examines an algorithm for collecting and analyzing data from wastewater treatment facilities, aimed at addressing regression tasks for predicting the quality of treated wastewater and classification tasks for preventing emergency situations, specifically filamentous bulking of activated sludge. The feasibility of using data obtained under laboratory conditions and simulating the technological process as a training dataset is explored. A small dataset collected from actual wastewater treatment plants is considered as the test dataset. For both regression and classification tasks, the best results were achieved using gradient-boosting models from the CatBoost family, yielding metrics of SMAPE = 9.1 and ROC-AUC = 1.0. A set of the most important predictors for modeling was selected for each of the target features.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Hybrid model composed of machine learning and ASM3 predicts performance of industrial wastewater treatment

Cited by 3 publications

References 26 publications

Industrial activated sludge model identification using hyperparameter-tuned metaheuristics

Industrial activated sludge model identification using hyperparameter-tuned metaheuristics

Improving Prediction of Nutrient Recovery via Struvite Precipitation from Organic Waste Digestate

Machine Learning Methods for the Prediction of Wastewater Treatment Efficiency and Anomaly Classification with Lack of Historical Data

Contact Info

Product

Resources

About