Artificial neural network model for the flow regime recognition in the drying of guava pieces in the spouted bed

Abstract

In this article, an artificial neural network model (ANN) was developed for the flow regime recognition in the spouted bed dryer. Instabilities and changes were observed in the hydrodynamics of the bed during the drying of guava pieces with deformability and variation in physical properties. Changes in the Archimedes number and Littman parameter directly affected the hydrodynamics of the bed. Experimental data on the variation of the properties of the dried guava pieces were used to obtain the fluid dynamics parameters this was also used as an input data in the ANN model whereas the operating regime of the spouted bed dryer, fixed-,fluidized-,spouted-, and slugging beds were the output model variables. The architecture of the neural network model was selected using the particle swarm optimization algorithm (PSO). The optimized neural model achieved a recognition accuracy of 86% for the fixed and fluidized beds and 99% for the spouted and slugging beds.