Modeling Thermal Conductivity of Iranian Flat Bread Using Artificial Neural Networks

Abstract

The objective of this work was to develop Artificial Neural Network (ANN) based thermal conductivity (K) prediction model for Iranian flat breads. Experimental data needed for ANN models were obtained from a pilot-scale set-up. Breads were made from three different cultivars of wheat and were baked in an eclectic oven at three different baking temperatures (232°C, 249°C and 260°C). A data set of 205 conditions was used for developing ANN and empirical models. To model K using ANN, 16 different MLP (multilayer perceptron) configurations ranging from one to two hidden layers of neurons were investigated and their prediction performances were evaluated. The (4-3-5-1)-MLP network, that is a network having two hidden layers, with three neurons in its first hidden layer and five neurons in its second hidden layer, had the best results in predicting the thermal conductivity of flat bread. For this network, R2, MRE, MAE and SE were 0.988, 0.6323, 1.66×10− 3, and 8.56×10−4, respectively. Overall, ANN models (with R2 ≥ 0.95) performed superior than the empirical model (with R2 = 0. 870).