Artificial neural network modelling of moisture content evolution for convective drying of cylindrical quince slices

Abstract

In the present study, moisture content evolution of cylindrical quince slices during convective drying was modelled by using artificial neural networks (ANN). Quince slices with an average initial moisture content of 81% in wet basis (w.b.) or 4.27 kgwater/kgdry matter in dry basis (d.b.), were dried in a laboratory thermal convective dryer and experimental data of moisture content versus drying time was obtained for nine measurement groups of 40, 50 and 60 °C drying air temperature and 1, 2 and 3 m/s airflow velocity respectively. Different topologies of multilayer perceptron (MLP) ANN models containing a single or two hidden layers with a different number of hidden neurons and different types of transfer functions, have been investigated for predicting the moisture content evolution during drying. A group k-fold cross validation iteration procedure was performed for each developed ANN structure, in order to assess each model’s ability to estimate the moisture content of quinces on unseen data of air-drying temperature and airflow velocity combinations held out of the training process. For the cross validation of the developed ANN models, appropriate statistical evaluation indices were applied. The best performed ANN model based on the cross validation score metrics, contained two hidden layers with the sigmoid, softplus transfer functions and was composed by 90 artificial hidden neurons in each of the two hidden layers. A satisfying agreement of predictions with the experimental data was noticed, achieving coefficients of determination (R2) greater than 99% and root mean square error (RMSE) values less than 0.08 kgwater/kgdry matter.