Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using Artificial Neural Network

Momenzadeh, Leila; Zomorodian, Seyed Mohammad Ali; Mowla, Dariush

doi:10.1016/j.fbp.2010.03.007

Cited by 94 publications

(43 citation statements)

References 18 publications

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“…Drying of fruits and vegetables provides feasibility of their long-term storage and makes it possible for the consumers to select miscellaneous choices with varied flavors and textures (Henríquez et al 2013;Motavali et al 2013). Among different available drying methods, fluidized bed drying is a productive drying technique in the food industry and is considered as one of the most favorite drying methods to prepare completely and evenly dried food products (Momenzadeh et al 2011). Fluidized bed drying could be economically favorable if it could be equipped with a heat pump to save the energy usage for heating the drying air (Malekjani et al 2013;Hashemi Shahraki et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In other words, as these systems are based on learning techniques, determining the dependence rate of process parameters upon environmental situations is no longer necessary (Pedreño-Molina et al 2005;Giraldo-Zuniga et al 2006). Moreover, while in most cases the relationship among several variables describing drying characteristics of food products is fairly difficult to display, ANN can provide a platform and solve complex problems with high accuracy (Momenzadeh et al 2011). The quantity of each hidden neuron is a weighted linear combination of input neurons; output neurons receive an input number from each hidden neuron and transfer function 1 is applied to determine final output quantity.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion

Jafari

Ganje

Dehnad

et al. 2015

Journal of Food Processing and Preservation

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In this study, the influence of drying temperature (40, 50, 60C) and airflow velocity (2 and 3 m/s) on drying onion was evaluated by a custom designed fluidized bed dryer equipped with a heat pump dehumidifier. A comparative study was performed among nonlinear regression techniques, fuzzy logic and artificial neural networks to estimate the dynamic drying behavior of onion. Among nine mathematical models, approximation of diffusion with R 2 = 0.9999 and root mean square error = 0.004157 showed the best fit with experimental data. Fuzzy logic tool in MATLAB with Mamdani model in the form of If-Then rules along with triangular membership function used for simulation, interpolation and obtaining a theoretical increase in experimental moisture ratios were used. Feedforwardbackpropagation neural system with application of Levenberg-Marquardt training algorithm, hyperbolic tangent sigmoid transfer function, training cycle of 1,000 epoch and 2-5-1 topology was determined as the best neural model in terms of statistical indices. PRACTICAL APPLICATIONSForecasting kinetics of food drying by application of precise mathematical and dynamic modeling techniques as well as preparing particular patterns for describing such drying behaviors of diverse food products seem unavoidable for processing factories to optimize the quality of their dried food products and reduce operational costs. For this purpose, in this study, we applied different temperatures and airflow velocities to dry onion by a custom designed fluidized bed dryer equipped with a heat pump dehumidifier, predicting its drying behavior by regression, fuzzy logic and artificial neural network techniques and comparing accuracy of those models. Also, this paper compares prediction patterns of drying onion by fluidized bed drying with those of other food products dried by various drying methods. The results of this study will be helpful for all researchers and producers who want to know more about the nuances of impacts of different fluidized bed drying variables on the patterns of heat and mass transfer in fruits and vegetables.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion

Jafari

Ganje

Dehnad

et al. 2015

Journal of Food Processing and Preservation

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“…Poonnoy, Tansakul, and Chinnan (2006) predicted temperature and moisture content of tomato slices during drying by microwave-vacuum dryer. Momenzadeh, Zomorodian, and Mowla (2011) predicted drying time of corn shell during drying by concurrent microwave-fluidized bed dryer by ANNs. Madadlou et al (2009) predicted casein micelles size by combinative approach ANNs-RSM.…”

Section: Modeling Of Drying Kiwi Slices and Its Sensory Evaluationmentioning

confidence: 99%

Modeling of drying kiwi slices and its sensory evaluation

Mahjoorian

Mokhtarian

Fayyaz

et al. 2016

Food Science & Nutrition

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In this study, monolayer drying of kiwi slices was simulated by a laboratory-scale hot-air dryer. The drying process was carried out at three different temperatures of 50, 60, and 70°C. After the end of drying process, initially, the experimental drying data were fitted to the 11 well-known drying models. The results indicated that Two-term model gave better performance compared with other models to monitor the moisture ratio (with average R 2 value equal .998). Also, this study used artificial neural network (ANN) in order to feasibly predict dried kiwi slices moisture ratio (y), based on the time and temperature drying inputs (x 1 , x 2 ). In order to do this research, two main activation functions called logsig and tanh, widely used in engineering calculations, were applied.The results revealed that, logsig activation function base on 13 neurons in first and second hidden layers were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R 2 value .997. Furthermore, kiwi slice favorite is evaluated by sensory evaluation. In this test, sense qualities as color, aroma, flavor, appearance, and chew ability (tissue brittleness) are considered. K E Y W O R D SArtificial neural network, hot-air drying, sensory evaluation 1

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“…In 2008, scientists used the intelligent tools of the artificial neural network (AAN) to predict the freezing and defrosting time of food products (Goñi et al, 2008). Momenzadeh et al (2011) predicted the drying time of corn hulls with the simultaneous effect of microwave and fluid bed dryer systems in the neural networks design (Momenzadeh et al, 2011). Other groups of scientists have examined the moisture content, as well as the proportion of moisture content, to work out the freeze drying duration of apple slices (Menlik et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Screening of the aerodynamic and biophysical properties of barley malt

Ghodsvali¹,

Farzaneh

Bakhshabadi

et al. 2016

International Agrophysics

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A b s t r a c t. An understanding of the aerodynamic and biophysical properties of barley malt is necessary for the appropriate design of equipment for the handling, shipping, dehydration, grading, sorting and warehousing of this strategic crop. Malting is a complex biotechnological process that includes steeping; germination and finally, the dehydration of cereal grains under controlled temperature and humidity conditions. In this investigation, the biophysical properties of barley malt were predicted using two models of artificial neural networks as well as response surface methodology. Stepping time and germination time were selected as the independent variables and 1 000 kernel weight, kernel density and terminal velocity were selected as the dependent variables (responses). The obtained outcomes showed that the artificial neural network model, with a logarithmic sigmoid activation function, presents more precise results than the response surface model in the prediction of the aerodynamic and biophysical properties of produced barley malt. This model presented the best result with 8 nodes in the hidden layer and significant correlation coefficient values of 0.783, 0.767 and 0.991 were obtained for responses one thousand kernel weight, kernel density, and terminal velocity, respectively. The outcomes indicated that this novel technique could be successfully applied in quantitative and qualitative monitoring within the malting process.

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Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using Artificial Neural Network

Cited by 94 publications

References 18 publications

Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion

Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion

Modeling of drying kiwi slices and its sensory evaluation

Screening of the aerodynamic and biophysical properties of barley malt

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