Percorrer por autor "Golpour, Iman"
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- Diagnosing Fungal Infection in Wheat Kernels by Integrating Spectroscopic Technology and Digital Color Imaging System: Artificial Neural Network, Principal Component Analysis and Correlation Feature Selection TechniquesPublication . Zohrabi, Saman; Seiiedlou, Seyed Sadegh; Golpour, Iman; Lefsrud, Mark; Guiné, Raquel; Sturm, BarbaraContamination of cereal grain, especially wheat, with fungal infections can cause significant economic impacts and it endangers the health of humans and livestock. This study aims to appraise the UV/VIS–NIR and digital color (RGB) imaging systems and spectroscopic methodology to detect wheat kernels infected by fungi such as Penicillium expansum and Fusarium graminearum. NIR spectra of 190–1100 nm at 10 nm intervals, visible color reflectance images and non-visible reflectance images of wheat kernels in the ultraviolet and near-infrared ranges were applied to develop the multi-layer perceptron (MLP) artificial neural network model. The optimum wavelengths were selected by application of the principal component analysis (PCA) after preprocessing the raw spectra. A confusion matrix was used in the correlation feature selection method (CFS) for the decision tree classifier of selected features. The results showed that the four UV wavelengths of 310, 330, 400, and 410 nm were the best wavelengths using PCA to distinguish healthy and unhealthy wheat kernels. Considering the intensity of the wavelengths as the neural network inputs, samples were classified into healthy and unhealthy categories with an accuracy of 90.9%. Also, 18 features of color images in RGB, LAB, HSV, HSI, YCbCr, and YIQ spaces provided the highest average accuracy of 44.4% in classifying healthy and infected wheat kernels by using a CCD Proline camera in the ultraviolet range. In contrast, other cameras in the visible and invisible range showed low accuracy. Furthermore, the best classification accuracy of the healthy and infected samples by the use of the CFS method was obtained at 88.1%. Based on the findings, spectroscopic methodology proved to be highly effective for detecting, classifying and automatic cleaning of various agricultural seeds, with a particular emphasis on wheat kernals.
- Evaluating the heat and mass transfer effective coefficients during the convective drying process of paddy ( Oryza sativa L.)Publication . Golpour, Iman; Guiné, Raquel P. F.; Poncet, Sébastien; Golpour, Hossein; Amiri Chayjan, Reza; Amiri Parian, JafarThis work investigates important heat and mass transfer parameters of a variety of paddy named Fajr, during a thin layer convective drying. The experiments on drying are conducted at air temperatures in the domain of 30–80 C with air velocities between 0.54 and 3.27 m s1 . Mathematical equations are developed to adjust the experimental data obtained for the thin layer drying. The drying results show that the Henderson and Pabis' model satisfactorily fit all the experimental data for the convective drying behavior of paddy. The effective diffusivity of moisture transfer, calculated by the Fick's second law of diffusion, varies from 1.109 1011 to 5.858 1010 m2 s 1 and the highest values of the Dincer, Biot, Reynolds, and Nusselt numbers obtained are 2.383 105 ; 0.2026; 1.833 107 ; and 8,434, respectively. The obtained results demonstrate that the coefficient of mass transfer (hm) for paddy lies between 5.118 108 and 4.807 106 m s1 , while the heat transfer coefficient (hc) ranges from 811 to 2,474 W m2 K1 . Furthermore, the values of the energies of activation obtained for moisture diffusion (Ed) and convective mass transfer (Ec) are in the range of 50.538–61.825 kJ mol1 and 72.325–87.386 kJ mol1 , respectively. Therefore, knowledge regarding to the determination process of heat and mass transfer characteristics can help to distinguish the suitable operating conditions for saving the maximum value of energy. Practical Applications Some grains have a short shelf life that limits their commercialization as fresh products and increase postharvest losses. Drying is an interesting alternative to the development of new products of grains like paddy with added value. This research work involved the estimation of heat and mass transfer properties during convective drying by using two calculation methodologies, allowing estimating the diffusivity and the mass and heat transfer coefficients for paddy. Also, the activation energy for moisture diffusion and for convective mass transfer was determined, assuming that the temperature dependence of the diffusion coefficient and the mass transfer coefficient follows an Arrhenius type relationship. Therefore, also the study presents a simple method to greatly enhance the shelf life of wet paddy by convective drying and it can be applied for the better preservation of this product.
- Investigating shrinkage and moisture diffusivity of melon seed in a microwave assisted thin layer fluidized bed dryerPublication . Golpour, Iman; Nejad, Moein; Chayjan, Reza; Nikbakht, Ali; Guiné, Raquel; Dowlati, MajidThis work aimed to investigate the drying behavior of melon seed during combined fluidized bed-microwave drying system. Three drying air temperatures (40, 55 and 70 C), three microwave powers (270, 450 and 630 W) and three air velocities (0.8, 1.5 and 2.3 m/s) were tested. Five mathematical models were selected to fit the experimental data for drying kinetics, and the results revealed that the Aghbashloo et al. model exhibited, in all cases, the best per- formance in fitting the experimental data (R 2 varying from 0.99088 to 0.99998; v 2 from 0.00000 to 0.00185 and RMSE from 0.02289 to 0.82316). Calculated values of moisture diffusivity for dried melon seed varied from a minimum of 6.51 9 10-10 to a maximum of 6.59 9 10-9 m 2 /s under the tested drying conditions. Moisture diffusivity values increased as air temperature and microwave power was increased. Shrinkage values were calculated and found to vary in the range from 46.99 to 15.09 %.
- 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.