Hybrid-neural modeling for particulate solid drying processes

In this work, a general framework for modeling and simulation of particulate solid drying processes is presented, based on fundamental conservation laws associated with a neural network, for model uncertain parameter estimation. The modeling approach, which leads to a hybrid-neural model, is applied in order to describe the dynamic behavior of two important drying systems: a direct flow rotary dryer and a batch fluidized bed dryer. Both models are built using simple mass and energy balances, where heat and mass transfer parameters are estimated with neural networks. Model behavior was evaluated by comparing experimental and simulation data. It is concluded that the hybrid-neural modeling approach is better for adaptation and prediction than its black box type counterpart.