Finite element modeling in heat and mass transfer of potato slice dehydration, nonisotropic shrinkage kinetics using arbitrary <scp>Lagrangian–Eulerian</scp> algorithm and artificial neural network
Rahul Das, K. Prasad
Abstract
Abstract The present study optimized the drying temperature (50–80°C) for potato slices based on color, texture, and visual observations. At an optimized temperature (60°C), a 2D axisymmetric finite element method (FEM) was developed in COMSOL Multiphysics to predict the heat and mass transfer (HMT) in a disk‐shaped potato slice. The nonisotropic shrinkage was predicted for the potato slice by the arbitrary Lagrangian–Eulerian approach. The experimental dehydration results revealed that axial shrinkage (27.44%) was 2.5 times higher than radial shrinkage (67.39%). The simulated outcomes based on FEM revealed the realistic visualization of spatial heat transfer, moisture migration, and nonisotropic slice deformation. The predicted moisture content, surface temperature, and shrinkage properties were in good agreement with the experimental results. The shrinkage behavior was further validated using artificial neural network (ANN) to simulate the slice shrinkage. Results showed that both the COMSOL and ANN approaches can precisely predict the shrinkage‐dependent HMT model. The ANN model outperformed the COMSOL determined by mean absolute error, mean square error (MSE), root MSE, and Chi‐square (χ 2 ) values. The successful application of the presented approach for determining dehydration characteristics may have potential for quality assessment and management of different fruits and vegetables. Practical applications This journal article explores the practical industrial applications of combining finite element method (FEM)‐based heat and mass transfer model and artificial neural networks (ANNs) to improve the efficiency and quality of food drying processes. FEM is employed to simulate and predict the realistic visualization of heat and mass transfer phenomena along with non‐isotropic shrinkage, while ANN serves as a data‐driven modeling tool for process control and prediction. The integration of these two technologies offers significant advantages in the food industry, including quantification of precise temperature and moisture content, as well as to monitor the drying process of various food products, reduced energy consumption, and time.