Biofilm rigidity, mechanics and composition in seawater desalination pretreatment employing ultrafiltration and microfiltration membranes

Ranieri, Luigi; Esposito, Rebecca; Nunes, Suzana P.; Vrouwenvelder, Johannes S.; Fortunato, Luca

doi:10.1016/j.watres.2024.121282

Water Research

2024

DOI: 10.1016/j.watres.2024.121282

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Biofilm rigidity, mechanics and composition in seawater desalination pretreatment employing ultrafiltration and microfiltration membranes

Luigi Ranieri,

Rebecca Esposito,

Suzana P. Nunes

et al.

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Cited by 4 publications

References 62 publications

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Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives

Cairone,

Hasan,

Choo

et al. 2024

Euro-Mediterr J Environ Integr

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Membrane filtration processes have demonstrated remarkable effectiveness in wastewater treatment, achieving high contaminant removal and producing high-quality effluent suitable for safe reuse. Membrane technologies play a primary role in combating water scarcity and pollution challenges. However, the need for more effective strategies to mitigate membrane fouling remains a critical concern. Artificial intelligence (AI) modeling offers a promising solution by enabling accurate predictions of membrane fouling, thus supporting advanced fouling mitigation strategies.This review examines recent progress in the application of AI models, with a particular focus on artificial neural networks (ANNs), for simulating membrane fouling in wastewater treatment processes. It highlights the substantial potential of ANNs, particularly the widely studied multi-layer perceptron (MLP) and other emerging configurations, to accurately predict membrane fouling, thereby enhancing process optimization and fouling mitigation efforts. The review discusses both the potential benefits and current limitations of AI-based strategies, analyzing recent studies to offer valuable insights for designing ANNs capable of providing accurate fouling predictions. Specifically, it provides guidance on selecting appropriate model architectures, input/output variables, activation functions, and training algorithms. Finally, this review highlights the critical need to connect research findings with practical applications in full-scale wastewater treatment plants. Key steps crucial to address this challenge have been identified, emphasizing the potential of AI modeling to revolutionize process control and drive a paradigm shift toward more efficient and sustainable membrane-based wastewater treatment.

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Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives

Cairone,

Hasan,

Choo

et al. 2024

Euro-Mediterr J Environ Integr

View full text Add to dashboard Cite

show abstract

Coupled mechanical and hydrodynamic 3D modelling to evaluate membrane intrusion impact on pressure drop in reverse osmosis permeate channels

Battaglia,

Ranieri,

Blankert

et al. 2024

Desalination

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In-situ observation and quantification of membrane deformation in reverse osmosis, using optical coherence tomography

Ranieri,

Fortunato,

Vrouwenvelder

et al. 2024

Desalination

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Biofilm rigidity, mechanics and composition in seawater desalination pretreatment employing ultrafiltration and microfiltration membranes

Cited by 4 publications

References 62 publications

Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives

Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives

Coupled mechanical and hydrodynamic 3D modelling to evaluate membrane intrusion impact on pressure drop in reverse osmosis permeate channels

In-situ observation and quantification of membrane deformation in reverse osmosis, using optical coherence tomography

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