Improve the energy efficiency of the fruit freeze-drying through the predictive analysis

Abstract

The considerable energy expenditure involved in the freeze-drying of foods justifies the development of innovative engineering techniques. Artificial intelligence will facilitate mass balance and energy efficiency in future food freeze-drying processes. This study assessed the energy and manufacturing efficiency of the freeze-drying facility through the application of artificial intelligence. Two distinct artificial neural network models were created utilizing real-time data from a factory located in an industrial zone that processed freeze-dried vegetables and kiwi fruit. Analyzing energy efficiency values and production was done using network models constructed from 20 experimental data sets. The Levenberg-Marquardt approach was employed to train neural networks with a multilayer perceptron architecture. The neural network models' prediction values were compared with the experimentally acquired data, and their performance was examined using several performance criteria. The evaluations carried out for 20 different scenarios revealed overall energy efficiency rates ranging from 25.8 % to 64.5 %. The considerable energy expenditure involved in the freeze-drying of foods justifies the development of innovative engineering techniques. Artificial intelligence will facilitate mass balance and energy efficiency in future food freeze-drying processes.