Modelling of transmembrane pressure using slot/pore blocking model, response surface and artificial intelligence approach

Khan, Hassan; Khan, Saad Ullah; Hussain, Sajjad; Ullah, Asmat

doi:10.1016/j.chemosphere.2021.133313

Cited by 6 publications

(1 citation statement)

References 40 publications

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“…As discussed in previous sections on ML, ANNs are the predominant model used in water/wastewater-treatment applications. For simulating the membrane-filtration processes, ANNs such as RNNs (some of which contain LSTM), as well as ANFIS and SVM, have also been utilized [69]. Table 5 showcases the applications that employ ML to model, predict, and enhance the membrane-filtration process.…”

Section: Membrane-filtration Proceduresmentioning

confidence: 99%

Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Alprol,

Mansour,

Ibrahim

et al. 2024

Water

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Integration of the Internet of Things (IoT) into the fields of wastewater treatment and water quality prediction has the potential to revolutionize traditional approaches and address urgent challenges, considering the global demand for clean water and sustainable systems. This comprehensive article explores the transformative applications of smart IoT technologies, including artificial intelligence (AI) and machine learning (ML) models, in these areas. A successful example is the implementation of an IoT-based automated water quality monitoring system that utilizes cloud computing and ML methods to effectively address the above-mentioned issues. The IoT has been employed to optimize, simulate, and automate various aspects, such as monitoring and managing natural systems, water-treatment processes, wastewater-treatment applications, and water-related agricultural practices like hydroponics and aquaponics. This review presents a collection of significant water-based applications, which have been combined with the IoT, artificial neural networks, or ML and have undergone critical peer-reviewed assessment. These applications encompass chlorination, adsorption, membrane filtration, monitoring water quality indices, modeling water quality parameters, monitoring river levels, and automating/monitoring effluent wastewater treatment in aquaculture systems. Additionally, this review provides an overview of the IoT and discusses potential future applications, along with examples of how their algorithms have been utilized to evaluate the quality of treated water in diverse aquatic environments.

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Section: Membrane-filtration Proceduresmentioning

confidence: 99%

Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Alprol,

Mansour,

Ibrahim

et al. 2024

Water

View full text Add to dashboard Cite

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Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks

Cifuentes-Cabezas

Bohórquez-Zurita

Gil-Herrero

et al. 2023

Food Bioprocess Technol

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Olive oil production generates a large amount of wastewater called olive mill wastewater. This paper presents the study of the effect of transmembrane pressure and cross flow velocity on the decrease in permeate flux of different ultrafiltration membranes (material and pore size) when treating a two-phase olive mill wastewater (olive oil washing wastewater). Both semi-empirical models (Hermia models adapted to tangential filtration, combined model, and series resistance model), as well as statistical and machine learning methods (response surface methodology and artificial neural networks), were studied. Regarding the Hermia model, despite the good fit, the main drawback is that it does not consider the possibility that these mechanisms occur simultaneously in the same process. According to the accuracy of the fit of the models, in terms of R2 and SD, both the series resistance model and the combined model were able to represent the experimental data well. This indicates that both cake layer formation and pore blockage contributed to membrane fouling. The inorganic membranes showed a greater tendency to irreversible fouling, with higher values of the Ra/RT (adsorption/total resistance) ratio. Response surface methodology ANOVA showed that both cross flow velocity and transmembrane pressure are significant variables with respect to permeate flux for all membranes studied. Regarding artificial neural networks, the tansig function presented better results than the selu function, all presenting high R2, ranging from 0.96 to 0.99. However, the comparison of all the analyzed models showed that depending on the membrane, one model fits better than the others. Finally, through this work, it was possible to provide a better understanding of the data modelling of different ultrafiltration membranes used for the treatment of olive mill wastewater.

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Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and standalone machine learning algorithms

Usman,

Abba,

Usman

et al. 2024

Journal of the Taiwan Institute of Chemical Engineers

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Modelling of transmembrane pressure using slot/pore blocking model, response surface and artificial intelligence approach

Cited by 6 publications

References 40 publications

Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks

Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and standalone machine learning algorithms

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