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  • A novel zero-liquid discharge desalination system based on the humidification-dehumidification process: a preliminary study

    Chen, Qian; Akhtar, Faheem; Burhan, Muhammad; Kumja, M; Ng, Kim Choon (Water Research, Elsevier BV, 2021-10-25) [Article]
    As a byproduct of desalination plants, brine is increasingly becoming a threat to the environment, and the design of zero-liquid discharge desalination (ZLDD) systems is gaining increasing attention. Existing ZLDD systems are limited by a high energy intensity and high plant costs of their crystallizers. This study proposes a novel crystallization method based on the humidification-dehumidification (HDH) process, which exhibits the advantages of a low energy consumption, low component costs and a reduced scaling and fouling potential. A simple experimental setup is first designed to demonstrate the feasibility of the proposed system. Brine concentration and salt crystallization are successfully achieved with air heated to 40 ℃ as the heat source. Afterwards, a thermo-economic analysis is conducted for the whole system. The specific thermal energy and electricity consumption levels are found to range from 700-900 and 5-11 kJ, respectively, per kg of feed brine. The energy consumption is 56% lower than that of a conventional evaporative crystallizer, and the initial plant cost is reduced by 58%. Keywords: zero-liquid discharge desalination; crystallization; humidification-dehumidification.
  • Local combustion regime identification using machine learning

    Malpica Galassi, Riccardo; Ciottoli, Pietro P.; Valorani, Mauro; Im, Hong G. (Combustion Theory and Modelling, Informa UK Limited, 2021-10-24) [Article]
    A new combustion regime identification methodology using the neural networks as supervised classifiers is proposed and validated. As a first proof of concept, a binary classifier is trained with labelled thermochemical states obtained as solutions of prototypical one-dimensional models representing premixed and nonpremixed regimes. The trained classifier is then used to associate the regime to any given thermochemical state originating from a multi-dimensional reacting flow simulation that shares similar operating conditions with the training problems. The classification requires local information only, i.e. no gradients are required, and operates on reduced-dimension thermochemical states, in order to cope with experimental data as well. The validity of the approach is assessed by employing a two-dimensional laminar edge flame data as a canonical configuration exhibiting multi-regime combustion behaviour. The method is readily extendable to additional classes to identify criticality phenomena, such as local extinction and re-ignition. It is anticipated that the proposed classifier tool will be useful in the development of turbulent multi-regime combustion closure models in large scale simulations.
  • A versatile framework to solve the Helmholtz equation using physics-informed neural networks

    Song, Chao; Alkhalifah, Tariq Ali; Waheed, Umair bin (Geophysical Journal International, Oxford University Press (OUP), 2021-10-23) [Article]
    Solving the wave equation to obtain wavefield solutions is an essential step in illuminating the subsurface using seismic imaging and waveform inversion methods. Here, we utilize a recently introduced machine-learning based framework called physics-informed neural networks (PINNs) to solve the frequency-domain wave equation, which is also referred to as the Helmholtz equation, for isotropic and anisotropic media. Like functions, PINNs are formed by using a fully-connected neural network (NN) to provide the wavefield solution at spatial points in the domain of interest, in which the coordinates of the point form the input to the network. We train such a network by back propagating the misfit in the wave equation for the output wavefield values and their derivatives for many points in the model space. Generally, a hyperbolic tangent activation is used with PINNs, however, we use an adaptive sinusoidal activation function to optimize the training process. Numerical results show that PINNs with adaptive sinusoidal activation functions are able to generate frequency-domain wavefield solutions that satisfy wave equations. We also show the flexibility and versatility of the proposed method for various media, including anisotropy, and for models with strong irregular topography.
  • Composite nanofiltration membrane comprising one-dimensional erdite, two-dimensional reduced graphene oxide, and silkworm pupae binder

    Balaji, K.R.; Hardian, Rifan; Dileep Kumar, V.G.; Viswanatha, R.; Kumar, Suneel; Kumar, Surender; Singh, Archana; Santosh, Mysore Sridhar; Szekely, Gyorgy (Materials Today Chemistry, Elsevier, 2021-10-23) [Article]
    Composite nanofiltration membranes offer advantages because of synergetic effects among the constituent materials’ properties. However, the sustainability of both the membrane fabrication and the raw materials has been a drawback of this energy-efficient separation technology. We report the facile fabrication of a nanocomposite membrane composed of a two-dimensional (2D) material of reduced graphene oxide (rGO) combined with a one-dimensional (1D) material of a ternary metal-based chalcogenide (NaFeS2 or NFS), using silkworm pupae protein as a natural binder. All the source materials can be derived from either nature or waste, ensuring the sustainability of the membrane and its production method. The structural characteristics of the synthesized membranes were analyzed, and the morphology of the composite membranes was studied thoroughly. Thermogravimetric analysis, differential scanning calorimetry, and nanoindentation characterizations indicated that the composite membranes were mechanically and thermally stable. The water and acetone fluxes; salt, dye, and pollutant rejections; and long-term membrane performance were evaluated using a cross-flow filtration system. Solute rejection was observed to increase (up to 98%, 94%, 95%, and 78% for Rhodamine B, 2,4-dichlorophenol, MgCl2, and NaCl, respectively) with increasing concentration of the nanomaterials in the membrane. The fine-tuning of the molecular weight cut-off from 794 to 600 g mol–1 was achieved by varying the concentration of the nanomaterials from 1 to 3 mg mL–1 . Our research findings demonstrate the synergetic effects of combining 1D and 2D materials using silkworm pupae binder. The composite membrane was stable in different classes of organic solvents, including hydrocarbons, alcohols, esters, ethers, polar aprotic solvents, halogenated solvents, and ketones. This first use of natural pupae binder in constructing membrane materials paves the way toward the development of more sustainable membranes.
  • COP26 Futures We Want – Regional Profile for the Arabian Peninsula

    Short, Samuel; Molini, Annalisa; Santamarina, Carlos; Friedrich, Luiz; Shuckburgh, Emily (Cambridge University Press (CUP), 2021-10-23) [Preprint]
    This regional profile for the Arabian Peninsula was developed in the context of the BEIS COP26 Futures We Want project. It covers the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA), and has been developed with the input from in- country academic experts Prof. Annalisa Molini and Mr Luiz Friedrich (Khalifa University, UAE) and Prof. Juan Carlos Santamarina (King Abdullah University of Science and Technology, KSA). It sets out a synthesis of the available evidence base on regional challenges and opportunities for mitigation, adaptation, and resilience measures for both KSA and UAE and the wider Arabian Peninsula associated with climate change and a global transition to an inclusive, desirable, and resilient net-zero future.

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