Comparison between Thin-Layer Models and Non-Traditional Methods in the Modelling of Drying Kinetics of Crustacean Wastes

HIGHLIGHTS

Drying kinetics of shrimp shell and crab exoskeleton were obtained experimentally

The Page and Midilli models showed the best fitting results to simulate the experimental data

Third-order models were obtained for both materials using the stepwise fit method

The ANNs showed the best fitting performance to describe the experimental drying curves

Modeling of the drying process can be simulated successfully by multivariable approaches.

Abstract

This research aims to compare the classical thin-layer models, stepwise fit regression method (SRG) and artificial neural networks (ANN) in the modelling of drying kinetics of shrimp shell and crab exoskeleton. Thus, drying curves were obtained using a convective dryer (3.0 m/s) at temperatures of 30.45 and 60^oC. The results showed a decreasing tendency for the drying time as the temperature increased for both materials. Drying curves modelling of both materials showed fitted results with R ² _adj >0.998 and MRE<13.128% for some thin-layer models. On the other hand, by SRG a simple model could be obtained as a function of time and temperature, with the greatest accuracy being found in the modelling of experimental data of crab exoskeleton, with MRE<10.149%. Finally, the ANNs were employed successfully in the modelling of drying kinetics, showing high prediction quality with the trained recurrent ANN models.

Keywords:
artificial neural network; crab exoskeleton; mathematical modelling; shrimp shell; statistical validation; stepwise fit regression