Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning

Yeo, Chester Su Hern; Xie, Qian; Zhang, Sui

doi:10.1016/j.memsci.2020.118135

Cited by 82 publications

(30 citation statements)

References 60 publications

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“…[63a,64] By optimizing the shape and functionality of polymer channels, higher performance of these MOFC membranes can be achieved. Advances in the fundamental understanding of ion transport through MOF frameworks, including computational simulation to predict and guide membrane design, [65] will facilitate the development of MOFbased membranes for practical ion separations.…”

Section: Discussionmentioning

confidence: 99%

Lithium Extraction by Emerging Metal–Organic Framework‐Based Membranes

Hou

Zhang

Thornton

et al. 2021

Adv Funct Materials

104

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Lithium is mainly extracted from brine and ores; however, current lithium mining methods require large amounts of chemicals, discharge many wastes, and can have serious environmental repercussions. Metal–organic framework (MOF)‐based membranes have shown great potential in lithium extraction due to their uniform pore sizes, high porosities, and rich host–guest chemistry compared to other materials. In this review, the processes and disadvantages of current lithium extraction technologies are introduced. The structure features and corresponding design strategy of MOFs suitable for Li+ ion separations are presented. Following, recent advances of polycrystalline MOF membranes, mixed matrix membranes, and MOF channel membranes for lithium‐ion separation are discussed in detail. Finally, opportunities for future developments and challenges in this emerging research field are presented.

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Section: Discussionmentioning

confidence: 99%

Lithium Extraction by Emerging Metal–Organic Framework‐Based Membranes

Hou

Zhang

Thornton

et al. 2021

Adv Funct Materials

104

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“…[101] Polymers [102] Thin film nanocomposite membranes [103] Heterogeneous, multicomponent materials [104] Memristors materials [105] Thermal functional materials [106] Mechanical metamaterials [107] Energy materials [108] Photonic crystals [109] Metal-organic nanocapsules [110] Hydrogels [111] Renewable energy materials [112] Alloys [113] Functional materials [114] Polymers [115] Ultraincompressible, superhard materials [116] Materials for clean energy [117] Photo energy conversion systems…”

Section: Referencesmentioning

confidence: 99%

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

2021

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Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.

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“…Nevertheless, AI and ML have previously been utilized for desalination and water and wastewater treatment processes using membrane technologies, such as membrane distillation, 25−30 reverse osmosis, 31−34 nanofiltration, 35−38 UF, 39,40 microfiltration, 41 thin-film nanocomposite membranes, 42 and membrane bioreactors. 35,43 ML was mainly used to predict membrane fouling and flux decline, for process optimization, in diffusion behavior studies, or to optimize fabrication parameters 44,45 limited to a specific local application or a data set to study or optimize the local variables or predict outputs. A few works have used combinatorial strategies for the search and optimization of novel membrane materials.…”

Section: Introductionmentioning

confidence: 99%

“…They employed a gradient boosting tree as a tree-based ML model to study the main factors on thin-film nanocomposite membranes. 45 In the methodology of Rall et al, accurate mass-transport models were used for the optimization of membrane processes. 48 ML is a subset of AI that can automatically learn a pattern using the obtained data in which the collection of them enables the device to provide an appropriate response based on a new set of data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes

Fetanat

Keshtiara

Low

et al. 2021

Ind. Eng. Chem. Res.

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Although the incorporation of nanoparticles into ultrafiltration polymeric membranes has shown promising outcomes, their commercial implementation has yet to be fulfilled due to inconsistency in data, lack of a reliable recipe for the optimum filler content, and reluctance in disrupting the production line which requires significant time and resources. There is a growing demand among membrane communities for a design platform that can accelerate the discovery of new nanocomposite membranes. In this work, a feed-forward ANN (artificial neural network) model that has one hidden layer and the Bayesian regularization training algorithm were chosen for designing a graphical user interface platform to predict the ultrafiltration nanocomposite membrane performance, that is, solute rejection, flux recovery, and pure water flux, thereby saving time and resources used in membrane design. Experimental data (735 samples from 200 reports published between 2006 and 2020) were derived from the literature for training, validation, and testing of the ANN models. The results indicated that the best 30 ANN models produce the most accurate estimation of membrane performance using the seven input variables of polymer concentration, polymer type, filler concentration, average filler size, solvent concentration (in the dope solution), solvent type, and contact angle on the unseen data set. Furthermore, a sensitivity analysis was performed on the achieved models to identify the most effective input variables for each nanocomposite membrane performance. This work has the potential to be extended to other mixed matrix membrane types that are going to be used for microfiltration, nanofiltration, reverse osmosis, and so forth.

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Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning

Cited by 82 publications

References 60 publications

Lithium Extraction by Emerging Metal–Organic Framework‐Based Membranes

Lithium Extraction by Emerging Metal–Organic Framework‐Based Membranes

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes

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