2 research outputs found

    Urea-Assisted Synthesis of Nanospherical and Plate-Like Magnesium Oxides for Efficient Removal of Reactive Dye Wastes

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    Nanospherical and plate-like magnesium oxide has been successfully synthesized by urea precipitation method for the first time. A magnesium oxide precursor was prepared by heating MgCl2 solution with urea for 12 hours at 90°C. Then the calcined precursor was analysed by Scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, thermogravimetric analysis (TGA), and high-resolution transmission electron microscopy (HR-TEM). In the presence of the nonionic surfactant Triton X-100 in the system, the reaction yielded in nanospheres of MgO contrast to the plate-like MgO in the absence of the surfactant. The precursor and the calcined product appeared in similar morphologies under SEM in both cases with a slight reduction of size upon calcination. The final product was confirmed as MgO using XRD and FT-IR spectroscopic methods. In TGA, both samples showed similar mass loss values upon elimination of adsorbed water molecules and decomposition of the precursor into MgO; however, the nanospherical MgO sample showed an additional weight loss due to elimination of the associated surfactant molecules. The efficiency of removing reactive dye wastes was quantified by UV-visible spectroscopy using reactive yellow dye. Plate-like MgO showed a porous structure under HR-TEM analysis in the dye adsorption study, and both plate-like and nanospherical MgO showed good dye adsorption capability. MgO nanospheres showed higher capacity of dye adsorption compared to plate-like MgO, explained by its higher surface are-to-volume ratio, while the plate-like MgO also performed well due to having a nanoporous structure. These nanomaterials will offer high potential in purifying waste water and as well in recovering expensive dye products

    Damage assessment in hyperbolic cooling towers using mode shape curvature and artificial neural networks

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    Hyperbolic cooling towers are large thin shell reinforced concrete structures that are used to remove the heat from wastewater and transfer it to the atmosphere using the process of evaporation. During its long service life, a cooling tower can experience damage due to the large temperature variations, environmental degradation, or random actions such as impacts or earthquakes. Such a damage can develop over time and result in the sudden collapse of the cooling tower. To ensure that a cooling tower operates safely and efficiently at all times, it is important to monitor its structural health. In this context, structural health monitoring based on the vibration characteristics of the structure, has emerged as a useful method to detect and locate damage in structures. Hyperbolic cooling towers, due to their particular shape, exhibit rather complex vibration characteristics that do not suit the traditional vibration-based damage detection techniques. This paper develops and applies a damage assessment method using the absolute changes in mode shape curvature (ACMSC) in conjunction with Artificial Neural Networks (ANNs) to detect, locate, and quantify damage in hyperbolic cooling towers. ANN is a machine learning technique that can predict behavioural patterns using a set of data samples and finds use in the damage quantification process. The proposed method for detecting and locating damage is experimentally validated and demonstrated its capability to accurately detect and locate damage. A feed-forward network having one hidden layer with Bayesian algorithm is used to train the artificial neural network. Damage indices calculated from noise polluted mode shape data are used to train the network. The trained network is then used to successfully assess the unknown damage severities in the cooling tower. The outcomes of this paper will enable early warning of damages in the cooling towers and will help towards their safe operation
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