There is scientific evidence that Stevia leaves, commonly used as a sugar substitute, can reduce the risk of obesity, diabetes, hypertension, or cardiovascular diseases. In this study, the moisture sorption isotherms of stevia leaves at 35°C, 45°C, and 55°C were analyzed by the static gravimetric method within a relative humidity range between 0.06 and 0.92. The experimental data obtained exhibits a J‐shaped Type II sorption isotherms. The data were fitted to six mathematical models: Iglesias & Chirife, Lewicki, Oswin, Hasley, GAB, and Khun. GAB model was the one best‐fitted to the isotherms obtained. The monolayer value was between 0.0479 and 0.0589 g water/g dry matter. The isosteric heat of adsorption and Gibbs free energy were obtained at different temperatures using the Clausius–Clapeyron equation. The isosteric heat of adsorption exponentially decreased from 2850 J/g to 2480 J/g as the equilibrium moisture content increased from 0.062 to 0.522 g water/g dry matter. The chemical potential of adsorption increased at high moisture content values. These results suggest an exothermic, spontaneous process.
Novelty Impact Statement
The importance of studying adsorption isotherms, and its fitting to a mathematical model, allows us to know the potential of the water that influences on the reactions of physical‐chemical deterioration correlated with the stevia leaves stability and quality. The thermodynamic properties derived from moisture adsorption isotherms provide relevant information about the water chemical potential and the energy amount required in moisture adsorption processes, and its relationship with stevia stability in different storage conditions.
The Freezing Melting (F/M) is considered one of the most sustainable desalination techniques in terms of energy consumption compared with commercial desalination. However, it presents some problems, especially salt trapping in the ice. In this paper, an experimental analysis is carried out to achieve a better understanding of saline ions displacement observed during the freezing of saline water. Different sizes, freezing temperatures and, geometries are tested using the F/M progressive desalination. It is found that salt diffusion improves with increasing the container length. Moreover, for all container geometries, the saline ion diffusion direction is from the bottom to the top of the formed ice. Furthermore, at low freezing temperature (−8°C), it is possible to purify 78.8 % of the frozen water, obtaining an easy-to-separate brine.
Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values.
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