A solvothermal method was used to prepare zinc ferrite spinel oxide (ZnFe2O4) using ethylene glycol and 1,4 butanediol as solvent diols, and the influence of diols on the physical properties of ZnFe2O4 particles was investigated. The produced particles were characterized by X-ray powder diffraction (XRD), atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR) and nitrogen adsorption isotherms, and the catalytic activity for the organic pollutant decomposition by heterogeneous photo-Fenton reaction was investigated. Both solvents produced particles with cubic spinel structure. Microporous and mesoporous structures were obtained when ethylene glycol and 1,4 butanediol were used as diols, respectively. A higher pore volume and surface area, as well as a higher catalytic activity for the pollutant degradation were found when 1,4 butanediol was used as solvent.
Maize drying is an important process, especially for storage and conservation. For this study, the experimental stage was carried out using a forced convection dryer with air heated at different temperature conditions (306.05–441.85 K) and flow (0.13–0.256 m3/hr), totalizing 15 drying curves. Then the performances of the classic drying kinetics methodology and the approach proposed in this paper, in which the increase in moisture content of the product with time was represented combining exponential models and neural networks based on wavelets, were compared. Good performance was obtained in predictions using the proposed approach. One of the main differentials of the methodology adopted was the obtainment of a model that has a global predictive capacity, within the range of tested operating conditions, which can be used in predicting drying curves for different operating conditions.
Practical applications
The drying process is also one of the most widely used methods for preserving food, and has the advantage of reducing the costs of storage and transport because of the low volume and weight of the end product. During the last years, this topic has attracted a broad industrial interest, resulting in many research studies investigating the drying process. Usually, with regard to the classic approach for modeling of the drying process, the kinetics of drying curves obtained in different operating conditions is affected separately, that is, the parameters are estimated independently, resulting in different regression problems. With the classical approach, in general, it is not possible to obtain a comprehensive prediction model with regards to operating conditions. We have proposed an alternative modeling method. Aiming to obtain a modeling tool with an overall predictive ability, an approach for drying kinetics prediction that combines exponential models and neural networks was proposed. The proposed modeling method was able to predict drying curves for different operating conditions.
The kinetics of the hot‐air drying of soybeans was modeled in order to evaluate the influence of temperature and velocity on the kinetic parameters. A convective dryer with air temperature from 30 to 195C and air flows of 0.75, 1.35, 2.0 and 2.5 m/s was used. Three different mathematical models were applied to simulate the drying process (two empirical equations, exponential and Page's, and Fick's diffusion model) and the diffusivity coefficient increased from 2.5 × 10−11 to 6.69 × 10−10 m2/s for a range of air temperature between 30 and 195C. Both temperature and velocity influenced drying rate. The differential evolution optimization method was used toward parameter estimation. The goodness of fit of the proposed models, evaluated using linear regression coefficient (R2), chi‐squared parameter (χ2) and root mean square error, indicated a satisfactory validation, mainly regarding to the exponential and Page's models.
Practical Applications
Although biological materials are dried to improve shelf life, reduce packaging costs and enhance sensorial aspects, they are highly susceptible to quality deterioration during dehydration if the processing parameters are not well adjusted. The mathematical modeling of food drying provides results about the influence of process parameters on energy efficiency and final product quality in order to help the optimization and upscale application. Given that up to 40% of the agro‐industrial production is lost in developing countries due to the lack of processing and that an energy efficiency improvement of 1% may result in 10% increase in profit, it is important to explore the potential of mathematical tools to properly study drying processes under an energy and qualitative approach.
This study evaluated the operational conditions that maximize the production of biogas from the use of digesters. Experimental tests were conducted using termination phase swine wastes, with total . Regarding the use of nutrients in the anaerobic digestion process, the results showed that there was significant reduction in hydraulic retention time and increased biogas productivity.
Neste trabalho, foi realizado um estudo para avaliar as principais variáveis de processo na produção de uma bebida fermento-destilada a partir do pseudofruto da uva-japão (Hovenia dulcis Thunberg). Foram avaliados os efeitos da temperatura, do tempo de fermentação e da adição de micronutrientes (Mn+2 e K+) sobre a graduação alcoólica, além do pH e dos teores de ésteres presentes nas amostras. A partir dos resultados experimentais obtidos, constatou-se que o pH (pH médio da fração "coração" 5,35) da bebida fermento-destilada a partir da uva-japão não é afetado pelos fatores testados. Para a graduação alcoólica e a concentração de ésteres (2,81 ± 3,13 mg.100mL-1) presentes na fração "coração" da bebida produzida, os fatores mais significativos foram a temperatura e a concentração de Mn+2. A uva-japão se mostrou uma alternativa viável para a produção de uma bebida fermento-destilada ou como matéria-prima para a produção de bioálcool para usos diversos (95 a 100 GL).
-In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system.
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