Modeling of a continuous fluidized bed dryer using artificial neural networks

The work involves experimentation on drying of solids in a continuous fluidized bed dryer covering different variables like bed temperature, gas flow rate, solids flow rate and initial moisture content of solids. The data are modeled using artificial neural networks. The results obtained from artificial neural networks are compared with those obtained using Tanks-in-series model. It was found that results obtained from ANN fit the experimental data more accurately compared to the RTD model with less percentage error. This indicates a better fit of artificial neural networks to experimental data compared to various mathematical models.