Optimised ANN and SVR models for online prediction of moisture content and temperature of lentil seeds in a microwave fluidised bed dryer

Abstract

Online monitoring and control of the drying processes are necessary to maintain the final products’ quality attributes, especially when a microwave system is used to facilitate the drying process. Machine learning techniques could be a suitable and very accurate approach for modelling the drying process. Two machine learning techniques including Support Vector Regression (SVR) and Artificial Neural Network (ANN) were employed to predict lentil seeds’ temperature and moisture ratio during drying in a microwave fluidised bed dryer with inputs of microwave power (0–500 W), fluidising air temperature (50 °C and 60 °C) and drying time. Mean squared error (MSE) and the coefficient of determination (R2) were used to evaluate the performance of the models. One hidden layer and 10 hidden neurons, the logistic sigmoid transfer function for the hidden layer, and a linear function for the output layer were determined to be the best structure for the ANN. Weights and biases were found by training the network with Bayesian Regularisation (trainbr) and the optimum MSE and R2 of the test set were 3.8×10-5 and 0.999 respectively. Optimised SVR could also provide a good model to predict MR and temperature (overall MSE = 1.96 ×10-4 andR2 = 0.995), although ANN provided relatively more accuracy for the temperature data. Therefore, ANN could be utilised as an accurate tool for the prediction of MR and temperature of lentil seeds during microwave fluidised bed drying.