ABSTRACT
This chapter discusses the main concepts of static optimization and provides some methods of drying technology. Static optimization is an important tool for the increase of performance and efficiency of many drying processes. Generally used model structures in optimization include first-principles models, stemming directly from mass, momentum, and energy balances of the process, which provide a lot of insight on the inner workings of the process but are generally highly complex. Artificial neural networks (ANNs) provide a way to formalize process responses in a mathematical framework mimicking the working of neural networks found in animals and humans, with the ability of automated learning. Keshani et al. presented an application of ANN to study and predict the amount of wall deposit in the spray drying of lactose solution. Process inputs were inlet air temperature, the feed flow rate and the ratio of maltodextrin to lactose in the solution.
