Optimization of an Artificial Neural Network Topology for Predicting Drying Kinetics of Carrot Cubes Using Combined Response Surface and Genetic Algorithm

Aghbashlo, Mortaza; Kianmehr, Mohammad Hossein; Nazghelichi, Tayyeb; Rafiee, Shahin

doi:10.1080/07373937.2010.538819

Cited by 47 publications

(28 citation statements)

References 19 publications

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“…Aghbashlo et al . () analysed the variation in the kinetic of carrot drying with the independent variables time, air temperature, air velocity and cube size. Finally, Frías et al .…”

Section: Introductionmentioning

confidence: 57%

Optimisation of convective drying of carrots using selected processing and quality indicators

Gamboa‐Santos

Soria

Fornari

et al. 2013

Int J of Food Sci Tech

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Summary The effect of drying temperature (40–65 °C) and air rate (2–6 m s−1) on the formation of Maillard reaction indicators and vitamins content of carrots dehydrated by convection has been investigated. The range of assayed processing conditions, based on a previous central composite face design (CCD), led to moderate changes in the studied parameters, even under the most severe conditions. In addition, the drying kinetic of the process was studied taking into account the experimental quantitation of shrinkage, which allowed the determination of a first drying period with a constant rate of water evaporation per unit of exchange surface. The slope of the first drying period, the moisture loss during the first hour of drying and the level of quality parameters (Maillard reaction indicators and vitamins) were correlated with processing conditions with high accuracy. For the prototype here used, the optima temperature and air rate to maximise the desirability function (0.77) were 46 °C and 4.9 m s−1.

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“…Aghbashlo et al . () analysed the variation in the kinetic of carrot drying with the independent variables time, air temperature, air velocity and cube size. Finally, Frías et al .…”

Section: Introductionmentioning

confidence: 57%

Optimisation of convective drying of carrots using selected processing and quality indicators

Gamboa‐Santos

Soria

Fornari

et al. 2013

Int J of Food Sci Tech

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“…To avoid the parameter convergence this operation is essential (Fathi, Mohebbi, Razavi 2011). The proper convergence of GA is dependent upon its configuration which is a very important issue (Aghbashlo et al 2011). This convergence is made by defining the control parameters like the population size, the Chromosome size, mutation rate and crossover point.…”

Section: Mutationmentioning

confidence: 98%

The Optimization of Solar Drying of Grain by Using a Genetic Algorithm

Rahman

Mustayen

Mekhilef

et al. 2015

International Journal of Green Energy

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Particularly in the low drying temperature drying is an energy intensive process and requires a suitable optimization technique. This work represents the kinetic simulation and energy efficient optimization technique for drying long grain, i.e. rice. At 30-60 • C temperature the grains have been dried in a solar powered air dryer. After certain time interval the enzymatic activity and the moisture content have been measured. Genetic Algorithm (GA) has been used for the simulation and the optimization process while the experimental data have been used to fit the thin layer drying model. The results indicate that between 40 and 50 • C temperature and the 1.3-1.5 m/s air flow rate the drying time for the rice is around 120-180 min. The kinetic simulation and the quality analysis confirm that the dried products have a better enzymatic activity which resembles the quality standard and the required energy for the optimized drying process is lower than the others.

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“…Complex and highly nonlinear phenomena are involved in drying processes. [17,18] Youssefi et al presented a comparative study between an artificial neural network (ANN) and response surface methodology for modeling and prediction of quality parameters of spray-dried pomegranate juice. They indicated the superiority of a properly trained ANN in capturing the nonlinear behavior of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation of drying kinetics of pistachio nuts was proposed by Omid et al [17] Aghbashlo et al also investigated various multilayer perceptron (MLP) topologies to find the optimum topology and performed a comparative study among MLP results and empirical models. [18] Recently, ANNs have gained much popularity for simulating nonlinear relationships such as drying processes. ANNs permit adequate and precise control of the drying process in industrial applications and have been extensively used by many researchers.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Preprocessing Method for the Modeling and Prediction of Freeze-Drying Behavior of Apples: Multiple Output–Dependent Data Scaling (MODDS)

Polat

Kırmacı

2011

Drying Technology

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In the present study, the freeze drying behavior of apples have been modeled and predicted. Because freeze-drying is a very expensive and complex process, modeling of the freeze-drying process is a challenging task. In this study, a novel data scaling method called multiple output-dependent data scaling (MODDS) has been proposed and combined with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the moisture content (MC), moisture ratio (MR), and drying rate (DR) values, which are outputs of freeze-drying behavior of apples. The input parameters of the freeze drying system are the sample thicknesses, drying time, pressure, relative humidity, chamber temperature, and sample temperature. Using the input parameters, the outputs of the freeze-drying process of apples were predicted using a hybrid system based on MODDS and ANFIS. In the first stage, only input parameters were scaled using MODDS. In the second stage, the outputs of freeze drying of apples were predicted with the scaled input parameters using ANFIS algorithm. Ninety-two samples were included in the data set, including 10-, 7-, and 5-mm samples. In order to evaluate the performance of the proposed model, the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R 2 ), index of agreement (IA), and mean absolute percentage error (MAPE) were used. Though MSE values of 2.48, 0.035, and 0.011 and IA values of 0.887, 0.887, and 0.466 were obtained for MC, MR, and DR, respectively, using the ANFIS prediction algorithm the hybrid MODDS-ANFIS model achieved MSE values of 0.003, 0.00005, and 0.00007 and IA values of 0.999, 0.999, and 0.993 for the prediction of MC, MR, and DR, respectively. The results obtained demonstrate that the proposed hybrid system is a robust and efficient method for the modeling and prediction of freeze-drying behavior of apples.

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Optimization of an Artificial Neural Network Topology for Predicting Drying Kinetics of Carrot Cubes Using Combined Response Surface and Genetic Algorithm

Cited by 47 publications

References 19 publications

Optimisation of convective drying of carrots using selected processing and quality indicators

Optimisation of convective drying of carrots using selected processing and quality indicators

The Optimization of Solar Drying of Grain by Using a Genetic Algorithm

A Novel Data Preprocessing Method for the Modeling and Prediction of Freeze-Drying Behavior of Apples: Multiple Output–Dependent Data Scaling (MODDS)

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