Effluent parameters prediction of a biological nutrient removal (BNR) process using different machine learning methods: A case study

Neslihan Manav-Demir*, Huseyin Baran Gelgor, Ersoy Oz*, Fatih Ilhan, Kubra Ulucan-Altuntas, Abhishek Tiwary, Eyup Debik

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a novel targeted blend of machine learning (ML) based approaches for controlling wastewater treatment plant (WWTP) operation by predicting distributions of key effluent parameters of a biological nutrient removal (BNR) process. Two years of data were collected from Plajyolu wastewater treatment plant in Kocaeli, Türkiye and the effluent parameters were predicted using six machine learning algorithms to compare their performances. Based on mean absolute percentage error (MAPE) metric only, support vector regression machine (SVRM) with linear kernel method showed a good agreement for COD and BOD5, with the MAPE values of about 9% and 0.9%, respectively. Random Forest (RF) and EXtreme Gradient Boosting (XGBoost) regression were found to be the best algorithms for TN and TP effluent parameters, with the MAPE values of about 34% and 27%, respectively. Further, when the results were evaluated together according to all the performance metrics, RF, SVRM (with both linear kernel and RBF kernel), and Hybrid Regression algorithms generally made more successful predictions than Light GBM and XGBoost algorithms for all the parameters. Through this case study we demonstrated selective application of ML algorithms can be used to predict different effluent parameters more effectively. Wider implementation of this approach can potentially reduce the resource demands for active monitoring the environmental performance of WWTPs.

Original languageEnglish
Article number119899
JournalJournal of Environmental Management
Volume351
DOIs
Publication statusPublished - Feb 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Funding

The authors would like to acknowledge the contribution from Newton Fund via UK-Turkey British Council Research Environment Link Grant (ref. 630319963). The authors also thank Kocaeli Water and Sewerage Administration (ISU), Türkiye, for supporting this project. The authors would like to acknowledge the contribution from Newton Fund via UK-Turkey British Council Research Environment Link Grant (ref. 630319963 ). The authors also thank Kocaeli Water and Sewerage Administration ( ISU ), Türkiye, for supporting this project.

FundersFunder number
Kocaeli Water and Sewerage Administration
Newton Fund630319963
Illinois State University

    Keywords

    • Biological nutrient removal (BNR) process
    • Machine learning
    • Machine learning algorithms
    • Municipal wastewater treatment

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