Adaptive modeling of the drying of baker's yeast in a batch fluidized bed

Abstract

This study investigates the drying of baker's yeast in a fluidized-bed dryer. Mathematical modeling of the process was performed, incorporating the important process and quality parameters of the system. Artificial neural network (ANN) and adaptive neural network-based fuzzy inference system (ANFIS) structures were used to create process and quality models. Due to uncertainty regarding the process parameters, various different ANN structures were built, and the ANN with the optimum performance results for the proposed models was selected. This study also presents an ANFIS modeling approach with adaptive structure. ANN quality modeling was performed using process output parameters, and the quality loss incurred from drying the product was determined. These proposed models are easy to apply and do not impose any additional burden on the process (or the employees). The database used in this work was gathered from large quantities of industrial data (about 570 batches) obtained under various working conditions at random times over one year.