Two batch-to-batch (B2B) algorithms for supersaturation control in cooling crystallization are presented in this paper. In Iterative Learning Control (ILC) a nominal process model is adjusted with an additive correction term which depends on the error in the last batch. In Iterative Identification Control (IIC) the physical parameters of the process model are recursively estimated by adopting a Bayesian identification framework. Both B2B algorithms compute an optimized input for the next batch that is fed to a lower level PI feedback controller in order to reject the process disturbances. The tracking performance of these B2B+PI control schemes is investigated in a simulation study.