Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination?

Aani, Saif Al; Bonny, Talal; Hasan, Shadi W.; Hilal, Nidal

doi:10.1016/j.desal.2019.02.005

Cited by 164 publications

(71 citation statements)

References 75 publications

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“…(2) In contrast to the forces considered by Liu et al [28], the vectors of the shear force and the Coulomb force are perpendicular which means that friction has to be considered which is also challenging to approximate in this complex system and will likely involve empirical relations. Hence, a machine learning approach is adopted to approximate these phenomena since it is a proven methodology to model and optimise membrane separation processes [30][31][32][33]13].…”

Section: Nomenclaturementioning

confidence: 99%

Enhancing mechanistic models with neural differential equations to predict electrodialysis fouling

Jaegher

Schepper

Verliefde

2021

Separation and Purification Technology

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Section: Nomenclaturementioning

confidence: 99%

Enhancing mechanistic models with neural differential equations to predict electrodialysis fouling

Jaegher

Schepper

Verliefde

2021

Separation and Purification Technology

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“…Global population growth and increasing urbanization require novel economically viable concepts for drinking water treatment and water disposal [1]. This challenge includes smart operations, such as data-driven urban water management [2], but also new treatment concepts and technologies [3,4]. Synthetic membranes are an essential technological basis for this separation processes [5,6].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning

Rall

Schweidtmann

Kruse

et al. 2020

Journal of Membrane Science

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Innovative membrane technologies optimally integrated into large separation process plants are essential for economical water treatment and disposal. However, the mass transport through membranes is commonly described by nonlinear differential-algebraic mechanistic models at the nano-scale, while the process and its economics range up to large-scale. Thus, the optimal design of membranes in process plants requires decision making across multiple scales, which is not tractable using standard tools. In this work, we embed artificial neural networks (ANNs) as surrogate models in the deterministic global optimization to bridge the gap of scales. This methodology allows for deterministic global optimization of membrane processes with accurate transport modelsavoiding the utilization of inaccurate approximations through heuristics or shortcut models. The ANNs are trained based on data generated by a one-dimensional extended Nernst-Planck ion transport model and extended to a more accurate two-dimensional distribution of the membrane module, that captures the filtration-related decreasing retention of salt. We simultaneously design the membrane and plant layout yielding optimal membrane module synthesis properties along with the optimal plant design for multiple objectives, feed concentrations, filtration stages, and salt mixtures. The developed process models and the optimization solver are available open-source, enabling computational resource-efficient multi-scale optimization in membrane science.

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“…In membrane science, there exist a variety of publications describing process-related modeling using ANNs [22,23]. For instance, ANNs enable empirical predictions of fouling behavior [24][25][26][27], process parameters [28][29][30], and salt retention for nanofiltration systems [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous rational design of ion separation membranes and processes

Rall

Schweidtmann

Aumeier

et al. 2020

Journal of Membrane Science

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Economically viable water treatment process plants for drinking water purification are a prerequisite for sustainable supply of safe drinking water in the future. However, modern membrane process development experiences a disconnect in this domain: the synthesis of the membrane and the design of the process are decoupled. We propose an optimization strategy to simultaneously design the performance of layer-by-layer nanofiltration membrane modules and the separation process. This approach achieves overall optimal performance by extending the search space and thus exploiting synergies. Better separation performances at a lower cost as compared to conventional optimization strategies can be achieved. The key feature of this optimization framework is the integration of artificial neural networks. This machine-learning technique describes membrane performance as a function of its synthesis protocol. We optimize the design problem rigorously by a deterministic global nonlinear optimization method. Thus, this framework yields membrane synthesis protocols and membrane processes that are optimally tailored to the desired separation task. In a showcase, the simultaneous membrane synthesis and process optimization design achieve immediately favorable results with lower impurities at comparable costs. The process investment and operation costs are compared to a state of the art commercially available membrane for nanofiltration.

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Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination?

Cited by 164 publications

References 75 publications

Enhancing mechanistic models with neural differential equations to predict electrodialysis fouling

Enhancing mechanistic models with neural differential equations to predict electrodialysis fouling

Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning

Simultaneous rational design of ion separation membranes and processes

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