Experimental evaluation and modeling the mass and temperature of dried mint in greenhouse solar dryer; Application of machine learning method

Abstract

This study is aimed to model the temperature and mass of dried mint in a Quonset type of Greenhouse Solar Dryer (GSD). The inputs including ambient air temperature (◦C), ambient air humidity (%) and solar radiation (Wm-2) and output data including temperature (◦C) and mass (gr) of dried mint were collected from a Quonset GSD. Artificial Neural Network (ANN) models including Multilayer Perceptron (MLP) and Radial Bias Function (RBF) and also, Gaussian Process Regression (GPR) by k-fold cross validation method were used for modeling. Levenberg-Marquardt (LM) learning algorithm with Sigmoid-Tangent transfer function by different combinations of neurons in the hidden layer were assessed for ANN models. The results showed that MLP and GPR have higher error than RBF model for predicting the temperature and mass of dried mint. The results of RBF optimization indicated that 3-15-1 and 3-18-1 topologies with using 60 and 50% of total dataset for training steps and having 0.4 and 0.3 spread factor values can predict the temperature and mass of dried mint with Mean Absolute Percentage Error (MAPE) of 1.4 and 1.82%, respectively. The results of t, F, and Kolmogorov–Smirnov tests indicated that there is no significant difference between actual and RBF output values.