Characterization of loss factor and moisture diffusivity are required to understand materials' precise behavior during microwave processing. However, providing the processing facilities to measure these properties in a real or simulated situation directly can be complicated or unachievable. Hence, this study proposes an alternative procedure for modeling these properties according to their affecting factors including temperature, and moisture content. The basis of this method is to use an algorithm that combines the optimization approach and the numerical solution of the heat and mass transfer governing equations, including boundary conditions. For this aim, the coefficients of estimated models for loss factor and moisture diffusivity were obtained by minimizing the sum square error of the experimentally measured mean surface temperature and moisture content and the predicted values by solving the system of partial differential equations. The suggested models illustrated that during the microwave process, the moisture diffusivity grows arithmetically, and the loss factor generally raises, but transition points were observed in the trend for the samples tempered up to the 50% moisture content. These points have been attributed to the starch gelatinization and confirm how the bio-chemical reaction would have a noticeable effect on this property, determining the microwave energy absorbance. The results of differential scanning calorimetry thermograms and the Fourier transform mid-infrared spectra of flours obtained from microwave processed lentil seeds also confirmed the greatest intensity of starch structure alteration happened for the samples tempered to 50% moisture content by showing the highest shifts in the endothermic peak and lowest degree of order.
In this study, for the first time the activated carbon has been produced from medlar seed (Mespilus ger- manica) via chemical activation with KOH. The carbonization process was carried out at different tem- peratures of 450, 550, 650 and 750 °C. The Nitrogen adsorption-desorption, Fourier transform infrared spectroscopy (FTIR) and Field Emission Scanning Electron Microscope (FESEM) analyses were carried out on the adsorbents. The effect of operating parameters, such as pH, initial concentration of Cr(VI), adsor- bent dosage and contact time were investigated. The experimental data showed better agreement with the Langmuir model and the maximum adsorption capacity was evaluated to be 200 mg/g. Kinetic studies indicated that the adsorption process follows the pseudo second-order model and the chemical reaction is the rate-limiting step. Thermodynamic parameters showed that the adsorption process could be con- sidered a spontaneous ( G < 0), endothermic ( H > 0) process which leads to an increase in entropy ( S > 0). The application of support vector machine combined with genetic algorithm (SVM-GA) and ar- tificial neural network (ANN) was investigated to predict the percentage of chromium removal from aque- ous solution using synthesized activated carbon. The comparison of correlation coefficient (R 2 ) related to ANN and the SVR-GA models with experimental data proved that both models were able to predict the percentage of chromium removal, by synthetic activated carbon while the SVR-GA model prediction was more accurate.
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