MODEL IDENTIFICATION AND CONTROL STRATEGIES FOR BATCH COOLING CRYSTALLIZERS

The open-loop optimal control strategy to regulate the crystal-size di stribution of batch cooling crystallizers handles input, output, and f inal-time constraints, and is applicable to crystallization with size- dependent growth rate, growth dispersion, and fines dissolution. The o bjective function can be formulated to consider solid-liquid separatio n in subsequent processing steps. A model-based con trol algorithm req uires a model that accurately predicts system behavior. Uncertainty bo unds on model parameter estimates are not reported in most crystalliza tion model identification studies. This obscures the fact that resulti ng models are often based on experiments that do not provide sufficien t information and are therefore unreliable. A method for assessing par ameter uncertainty and its use in experimental design are presented. M easurements of solute concentration in the continuous phase and the tr ansmittance of light through a slurry sample allow reliable parameter estimation. Uncertainty in the parameter estimates is decreased by dat a from experiments that achieve a wide range of supersaturation. The s ensitivity of the control policy to parameter uncertainty, which conne cts the model identification and control problems, is assessed. The mo del identification and control strategies were experimentally verified on a bench-scale KNO3-H2O system. Compared to natural cooling, increa ses in the weight mean size of up to 48% were achieved through impleme ntation of optimal cooling policies.