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Authors = Grigoris Lykogiannis

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15 pages, 3663 KiB  
Review
Artificial Neural Networks in Membrane Bioreactors: A Comprehensive Review—Overcoming Challenges and Future Perspectives
by Zacharias Frontistis, Grigoris Lykogiannis and Anastasios Sarmpanis
Sci 2023, 5(3), 31; https://doi.org/10.3390/sci5030031 - 15 Aug 2023
Cited by 3 | Viewed by 3496
Abstract
Among different biological methods used for advanced wastewater treatment, membrane bioreactors have demonstrated superior efficiency due to their hybrid nature, combining biological and physical processes. However, their efficient operation and control remain challenging due to their complexity. This comprehensive review summarizes the potential [...] Read more.
Among different biological methods used for advanced wastewater treatment, membrane bioreactors have demonstrated superior efficiency due to their hybrid nature, combining biological and physical processes. However, their efficient operation and control remain challenging due to their complexity. This comprehensive review summarizes the potential of artificial neural networks (ANNs) to monitor, simulate, optimize, and control these systems. ANNs show a unique ability to reveal and simulate complex relationships of dynamic systems such as MBRs, allowing for process optimization and fault detection. This early warning system leads to increased reliability and performance. Integrating ANNs with advanced algorithms and implementing Internet of Things (IoT) devices and new-generation sensors has the potential to transform the advanced wastewater treatment landscape towards the development of smart, self-adaptive systems. Nevertheless, several challenges must be addressed, including the need for high-quality and large-quantity data, human resource training, and integration into existing control system facilities. Since the demand for advanced water treatment and water reuse will continue to expand, proper implementation of ANNs, combined with other AI tools, is an exciting strategy toward the development of integrated and efficient advanced water treatment schemes. Full article
(This article belongs to the Section Environmental and Earth Science)
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16 pages, 714 KiB  
Review
Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review
by Zacharias Frontistis, Grigoris Lykogiannis and Anastasios Sarmpanis
Environments 2023, 10(7), 127; https://doi.org/10.3390/environments10070127 - 19 Jul 2023
Cited by 16 | Viewed by 4944
Abstract
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 [...] Read more.
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. Full article
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