Relevance of Artificial Intelligence in Wastewater Management

Poornima, R.; Suganya, K.; Maheswari, T. Uma; Sebastian, S. Paul; Banu, K. Sara Parwin

doi:10.1002/9781119823469.ch14

Cited by 2 publications

(3 citation statements)

References 79 publications

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“…In addition, as in many industrial processes, control and optimization are vital to achieving stable and optimal performance. Monitoring and control include the measurement and adjustment of several process parameters, including the hydraulic retention time, sludge retention time, and nutrient dosing, to maintain optimal conditions for the removal of organic matter, simultaneously allowing for the accommodation of possible process disturbances and fluctuations in influent quality, while keeping TMP low to prevent fouling and increase the system's running time [8,13,16,17].…”

Section: Primary Challengesmentioning

confidence: 99%

“…Although ML can potentially improve the system performance and control, some challenges must still be solved before the wide implementation of these systems in MBRs. Among other challenges, these include: (i) data quality and availability, (ii) model interpretability, and (iii) adaptability to changes in process conditions [10,[12][13][14].…”

Section: Challenges In Applying Machine Learning To Mbr Systemsmentioning

confidence: 99%

“…At the same time, artificial intelligence has rapidly developed. In particular, machine learning (ML) has piqued the interest of several researchers towards the application of these advanced technologies to optimize and control various aspects of conventional or novel wastewater treatment processes [11][12][13][14]. As with several industrial processes, ML could enhance MBR efficiency using data-driven approaches for modeling and predicting the behavior of similar systems [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review

Frontistis,

Lykogiannis,

Sarmpanis

2023

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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.

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Section: Primary Challengesmentioning

confidence: 99%