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..