  The first and maybe the most important step in designing a model-based
predictive controller is to develop a model that is as accurate as possible and
that is valid under a wide range of operating conditions. The sugar boiling
process is a strongly nonlinear and nonstationary process. The main process
nonlinearities are represented by the crystal growth rate. This paper addresses
the development of the crystal growth rate model according to two approaches.
The first approach is classical and consists of determining the parameters of
the empirical expressions of the growth rate through the use of a nonlinear
programming optimization technique. The second is a novel modeling strategy
that combines an artificial neural network (ANN) as an approximator of the
growth rate with prior knowledge represented by the mass balance of sucrose
crystals. The first results show that the first type of model performs local
fitting while the second offers a greater flexibility. The two models were
developed with industrial data collected from a 60 m3 batch evaporative
crystallizer.
