Experimental Evaluation and Modeling of Strawberry Slices Drying Kinetics Based on Machine Learning

Abstract

The research explores the drying kinetics of strawberry slices (5 mm thick with initial moisture content of 88.04% wb) through the application of both traditional mathematical models and advanced machine learning method. The study aims to optimize the drying process by examining the effects of variables such as temperature, air velocity, and drying duration. Traditional models, derived from Fick's Second Law and Newton's Law of Cooling, were compared with artificial neural networks (ANN) and recurrent neural networks (RNN) to predict moisture content during the drying process. Ten network models were formed, and each model had three "hidden" layers with 20, 30, and 40 nodes in each layer. Findings revealed that RNN models, particularly RNN04, surpassed traditional models in accuracy, with a maximum deviation of up to 2% from experimental data. RNN models showed lower deviations in the range of 0.65% to 2%, while the ANN models had deviations in the interval of 2.6% to 5.6%. The ANN and RNN models included parameters like temperature, air flow speed, and drying time, with RNN models exhibiting superior adaptability and precision. These results indicate that machine learning approaches, especially RNNs, can greatly improve the understanding and management of the drying process, providing more precise and efficient methods for the drying industry.

Keywords

• artificial neural networks
• drying kinetics
• machine learning
• mathematical modelling
• recurrent neural networks
• strawberry slices