Dynamic Optimisation of Industrial Sugar Crystallization Process based on a Hybrid (mechanistic+ANN) Model

A model-based optimization of an industrial fed-batch sugar crystallisation process is considered in this paper. The objective is to define the optimal profiles of the manipulated process inputs, the feeding rate of liquor/syrup and the steam supply rate, such that the crystal content and the crystal size distribution (CSD) measures at the end of the batch cycle reach the reference values. A knowledge-based hybrid model is implemented, which combines a partial first principles model reflecting the mass, energy and population balances with an artificial neural network (ANN) to estimate the kinetics parameters - particle growth rate, nucleation rate and the agglomeration kernel. The simulation results demonstrate that the very tight and conflicting end-point objectives are simultaneously feasible in the presence of hard process constrains.