This paper is focused on issues of dynamic process modeling and model-based optimization of batch and fed-batch industrial crystallization processes applying the concept of artificial neural networks as computational tools. The objective is to drive the process to its optimal state of profit maximization and cost minimization. The simulation results demonstrate that the very tight and conflicting end-point objectives are simultaneously feasible in the presence of hard process constrains.
