Browsing by Author "Kaveh, Mohammad"
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- Multi-Response Design Optimisation of a Combined Fluidised Bed-Infrared Dryer for Terebinth (Pistacia atlantica L.) Fruit Drying Process Based on Energy and Exergy Assessments by Applying RSM-CCD ModellingPublication . Golpour, Iman; Kaveh, Mohammad; Blanco-Marigorta, Ana M.; Marcos, José Daniel; Guiné, Raquel; Chayjan, Reza Amiri; Khalife, Esmail; Karami, HamedThe present investigation aimed to perform an optimisation process of the thermodynamic characteristics for terebinth fruit drying under different drying conditions in a fluidised bed-infrared (FBI) dryer using response surface methodology (RSM) based on a central composite design (CCD) approach. The experiments were conducted at three levels of drying air temperature (40, 55, and 70 °C), three levels of drying air velocity (0.93, 1.765, and 2.60 m/s), and three levels of infrared power (500, 1000, and 1500 W). Energy and exergy assessments of the thermodynamic parameters were performed based on the afirst and second laws of thermodynamics. Minimum energy utilisation, energy utilisation ratio, and exergy loss rate, and maximum exergy efficiency, improvement potential rate, and sustainability index were selected as the criteria in the optimisation process. The considered surfaces were evaluated at 20 experimental points. The experimental results were evaluated using a second-order polynomial model where an ANOVA test was applied to identify model ability and optimal operating drying conditions. The results of the ANOVA test showed that all of the operating variables had a highly significant effect on the corresponding responses. At the optimal drying conditions of 40 °C drying air temperature, 2.60 m/s air velocity, 633.54 W infrared power, and desirability of 0.670, the optimised values of energy utilisation, energy utilisation ratio, exergy efficiency, exergy loss rate, improvement potential rate, and sustainability index were 0.036 kJ/s, 0.029, 86.63%, 0.029 kJ/s, 1.79 kJ/s, and 7.36, respectively. The models predicted for all of the responses had R2-values ranging between 0.9254 and 0.9928, which showed that they had good ability to predict these responses. Therefore, the results of this research showed that RSM modelling had acceptable success in optimising thermodynamic performance in addition to achieving the best experimental conditions.
- Optimization of Infrared-convective Drying of White Mulberry Fruit Using Response Surface Methodology and Development of a Predictive Model through Artificial Neural NetworkPublication . Golpour, Iman; Kaveh, Mohammad; Amiri Chayjan, Reza; Guiné, RaquelA comparative approach was carried out between artificial neural networks (ANNs) and response surface methodology (RSM) to optimize the drying parameters during infrared–con- vective drying of white mulberry. The drying experiments were performed at different air temperatures (40°C, 55°C, and 70°C), air velocities (0.4, 1, and 1.6 m/s), and three levels of infrared radiation power (500, 1000, and 1500 W). RSM focuses on the maximization of effective moisture diffusivity (D eff ) and minimi- zation of specific energy consumption (SEC) in the drying pro- cess. The optimized conditions were encountered for the air temperature of 70°C, the air velocity of 0.4 m/s, and the infrared power level of 1464.57 W. The optimum values of D eff and SEC were 1.77 × 10 −9 m 2 /s and 166.554 MJ/kg, respectively, with the desirability of 0.9670. Based on the statistical indices, the results showed that the feed and cascade-forward back-Propagation neural systems with application of Levenberg-Marquardt train- ing algorithm and topologies of 3–20-20-1 and 3–10-10-1 were the best neural models to predict D eff and SEC, respectively. This finding suggests that the ANN as an intelligent method with better performance compared to the RSM can be used to pre- dict the drying parameters of the infrared-convective drying of white mulberry fruit.