Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks

Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit

“…First, is the number of hidden layers and second is the number of neurons in each hidden layer. Since almost all of the problems in neural network modeling could be solved with one hidden layer (Chen et al 2001;Kashaninejad et al 2008;Mohebbi et al 2007;Movagharnejad and Nikzad 2007;Ochoa-Martínez and Ayala-Apaonte 2007;Mitra et al 2009), an ANN with three layers was used in this research. In addition, using too many hidden layers may lead to problem of data overfitting, affecting the system's generalization capability (Abdullah et al 2006).…”

The use of neural network to estimate mass transfer coefficient from the bottom of agitated vessel

“…Less work have been done for the case of mass transfer, of these the work of GarciaOchoa and Casto [36] and Lemoine et al [37][38][39] to estimate the oxygen volumetric mass transfer coefficient in stirred tank reactor. Also the ANN was used in many studies on separation [40][41][42][43][44][45] and drying processes [46][47][48][49].…”

Modular Feed Forward Networks to Predict Soil Penetration Resistance from Tillage Technique and Working Depth

“…The major benefits of such a technique include: modelling without any assumptions about the nature of the phenomological mechanisms underlying the process; the ability to learn linear and nonlinear relationships between variables and directly from a set of examples; the capacity of modeling multiple outputs simultaneously and; a reasonable application of the model to unlearned data (Ochoa and Ayala, 2006). The development of an ANN model involves: the generation of data required for the training/testing of the model, the actual training/testing of the ANN model, the evaluation of the ANN configuration leading to the selection of the optimal configurations and the validation of the optimal ANN model with a data set other than that used for training.…”

“…Once the ANN architecture was defined, the training was initiated and repeated several times to get the best performance (Ochoa and Ayala, 2006). The rule is to use at least 50% of the experiments at the training stage.…”