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      Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS.

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          Abstract

          This study aimed to predict the drying kinetics, energy utilization (Eu ), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (Deff ), Eu , EUR, exergy efficiency, and exergy loss. The value of the Deff was varied from 4.19 × 10-10 to 1.18 × 10-9 m2/s. The highest value Eu , EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R 2) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict Eu , EUR, exergy efficiency, and exergy loss, with R 2 of .9989, .9988, .9986, and .9978, respectively.

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          Convective drying of hawthorn fruit (Crataegus spp.): Effect of experimental parameters on drying kinetics, color, shrinkage, and rehydration capacity

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            Recent developments of artificial intelligence in drying of fresh food: A review

            Intellectualization is an important direction of drying development and artificial intelligence (AI) technologies have been widely used to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in different food drying technologies due to the advantages of self-learning ability, adaptive ability, strong fault tolerance and high degree robustness to map the nonlinear structures of arbitrarily complex and dynamic phenomena. This article presents a comprehensive review on intelligent drying technologies and their applications. The paper starts with the introduction of basic theoretical knowledge of ANN, fuzzy logic and expert system. Then, we summarize the AI application of modeling, predicting, and optimization of heat and mass transfer, thermodynamic performance parameters, and quality indicators as well as physiochemical properties of dried products in artificial biomimetic technology (electronic nose, computer vision) and different conventional drying technologies. Furthermore, opportunities and limitations of AI technique in drying are also outlined to provide more ideas for researchers in this area.
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              Comparison of energy parameters in various dryers

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                Author and article information

                Journal
                Food Sci Nutr
                Food science & nutrition
                Wiley
                2048-7177
                2048-7177
                Jan 2020
                : 8
                : 1
                Affiliations
                [1 ] Department of Biosystems Engineering College of Agriculture and Natural Resources University of Mohaghegh Ardabili Ardabil Iran.
                Article
                FSN31347
                10.1002/fsn3.1347
                6977499
                31993183
                c2e2e3a4-8bf3-48bb-a31f-d63754d4448b
                History

                adaptive neuro‐fuzzy inference system,thermodynamic parameters,quince,drying,artificial neural networks

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