Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review
This study offers a review of machine learning (ML) applications in membrane bioreactor (MBR) systems, an emerging technology in advanced wastewater treatment. The review focuses on implementing ML algorithms to enhance the prediction of membrane fouling, control and optimize the system, and predict...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Environments |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3298/10/7/127 |
| _version_ | 1797589382351290368 |
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| author | Zacharias Frontistis Grigoris Lykogiannis Anastasios Sarmpanis |
| author_facet | Zacharias Frontistis Grigoris Lykogiannis Anastasios Sarmpanis |
| author_sort | Zacharias Frontistis |
| collection | DOAJ |
| description | This study offers a review of machine learning (ML) applications in membrane bioreactor (MBR) systems, an emerging technology in advanced wastewater treatment. The review focuses on implementing ML algorithms to enhance the prediction of membrane fouling, control and optimize the system, and predict faults early, thereby enabling the development of novel cleaning strategies. Key ML algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), random forest, and reinforcement learning (RL) are briefly introduced, with an emphasis on their potential and limitations in advanced wastewater applications. The main challenges obstructing the implementation, namely data quality, interpretability, and transferability of ML, are identified. Finally, future research trends are proposed, including ML integration with big data, the Internet of Things (IoT), and hybrid model development. The review also underscores the need for interdisciplinary collaboration and investment in data management, along with the implementation of new policies addressing data privacy and security. By addressing these challenges, the integration of ML into MBRs has the potential to significantly enhance performance and reduce the energy footprint, providing a sustainable solution for advanced wastewater treatment. |
| first_indexed | 2024-03-11T01:05:50Z |
| format | Article |
| id | doaj.art-97fec2ab7aed4cb480afe0addf21259e |
| institution | Directory Open Access Journal |
| issn | 2076-3298 |
| language | English |
| last_indexed | 2024-03-11T01:05:50Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Environments |
| spelling | doaj.art-97fec2ab7aed4cb480afe0addf21259e2023-11-18T19:15:20ZengMDPI AGEnvironments2076-32982023-07-0110712710.3390/environments10070127Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A ReviewZacharias Frontistis0Grigoris Lykogiannis1Anastasios Sarmpanis2Department of Chemical Engineering, University of Western Macedonia, 50132 Kozani, GreeceECOTECH LTD., 17122 Athens, GreeceECOTECH LTD., 17122 Athens, GreeceThis study offers a review of machine learning (ML) applications in membrane bioreactor (MBR) systems, an emerging technology in advanced wastewater treatment. The review focuses on implementing ML algorithms to enhance the prediction of membrane fouling, control and optimize the system, and predict faults early, thereby enabling the development of novel cleaning strategies. Key ML algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), random forest, and reinforcement learning (RL) are briefly introduced, with an emphasis on their potential and limitations in advanced wastewater applications. The main challenges obstructing the implementation, namely data quality, interpretability, and transferability of ML, are identified. Finally, future research trends are proposed, including ML integration with big data, the Internet of Things (IoT), and hybrid model development. The review also underscores the need for interdisciplinary collaboration and investment in data management, along with the implementation of new policies addressing data privacy and security. By addressing these challenges, the integration of ML into MBRs has the potential to significantly enhance performance and reduce the energy footprint, providing a sustainable solution for advanced wastewater treatment.https://www.mdpi.com/2076-3298/10/7/127machine learning (ML)membrane bioreactor (MBR)wastewater treatmentcontrol and optimizationbig dataInternet of Things (IoT) |
| spellingShingle | Zacharias Frontistis Grigoris Lykogiannis Anastasios Sarmpanis Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review Environments machine learning (ML) membrane bioreactor (MBR) wastewater treatment control and optimization big data Internet of Things (IoT) |
| title | Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review |
| title_full | Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review |
| title_fullStr | Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review |
| title_full_unstemmed | Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review |
| title_short | Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review |
| title_sort | machine learning implementation in membrane bioreactor systems progress challenges and future perspectives a review |
| topic | machine learning (ML) membrane bioreactor (MBR) wastewater treatment control and optimization big data Internet of Things (IoT) |
| url | https://www.mdpi.com/2076-3298/10/7/127 |
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