Effect of conical air distributors on drying of peppercorns in a fluidized bed dryer: Prediction using an artificial neural network

Abstract

The effect of conical air distributors on the drying of peppercorns in a fluidized bed dryer was experimentally studied. A flat perforated sheet was installed in the column at the base of the bed. Conical air distributors consisted of two parts. The first was a solid cone located below an air duct, while the second part was a perforated metal cone placed on the flat perforated sheet. Experiments were carried out using perforated metal cones with three different height to base diameter ratios, (h/H) values of 0.5, 1.0, and 1.5 and three different air velocities, 1.2Umf, 1.6Umf, and 2.0Umf. An air distributor, consisting of a solid cone and a perforated metal cone with h/H = 1.0 and an air velocity of 2.0Umf, showed the best drying performance. It was also discovered that increasing the air velocity accelerated the drying process. A neural network was created to predict the moisture content of peppercorns during the drying process. The split, sample type, spilt ratio, momentum, and learning rate, as well as the numbers of hidden layers, hidden nodes, and training cycles all had an impact. A maximum coefficient of determination of 0.996 was found for the best model.