ABSTRACT
Drying is a unit operation largely found in many industries such as food, pharmaceutical, chemical, and waste treatment. Based on research developed at the Drying Center of Pastes, Seeds, and Suspensions, this chapter presents a few case studies in which artificial neural networks (ANNs) were used to predict drying parameters, physical properties, and phase coupling terms for the overall mass and energy balances applied to describe drying processes. In the first case study, an ANN was used to predict the drying kinetics of mint branches and of their fractions—leaves and stems. In the second case study, an ANN was used to predict the phase coupling term in a model designed to estimate the temperature and moisture dynamic profiles in spouted-bed drying pasty materials. Finally, in the third case study, the ANN was used as a tool to predict the higher heating values of biomass products, in the context of drying, based on a compilation of literature data.
