Solar-steam generation is one of the most promising technologies to mitigate the issue of clean water shortage using sustainable solar energy. Photothermal aerogels, especially the three-dimensional (3D) graphene-based aerogels, have shown unique merits for solar-steam generation, such as lightweight, high flexibility, and superior evaporation rate and energy efficiency. However, 3D aerogels require much more raw materials of graphene, which limits their large-scale applications. In this study, 3D photothermal aerogels composed of reduced graphene oxide (RGO) nanosheets, rice-straw-derived cellulose fibers, and sodium alginate (SA) are prepared for solar-steam generation. The use of rice straw fibers as skeletal support significantly reduces the need for the more expensive RGO by 43.5%, turning the rice straw biomass waste into value-added materials. The integration of rice straw fibers and RGO significantly enhances the flexibility and mechanical stability of the obtained photothermal RGO−SA−cellulose aerogel. The photothermal aerogel shows a strong broad-band light absorption of 96−97%. During solar-steam generation, the 3D photothermal aerogel effectively decreases the radiation and convection energy loss while enhancing energy harvesting from the environment, leading to an extremely high evaporation rate of 2.25 kg m −2 h −1 , corresponding to an energy conversion efficiency of 88.9% under 1.0 sun irradiation. The salinity of clean water collected during the evaporation of real seawater is only 0.37 ppm. The materials are environmentally friendly and cost-effective, showing great potential for real-world desalination applications.
Abstract:Accurate simulation and prediction of the dynamic behaviour of a river discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a river discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of river discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of river discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict river discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction.
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