A model-based control framework for industrial batch crystallization processes

Dynamic optimization is applied for throughput maximization of a semi-industrial batch crystallization process. The control strategy is based on a non-linear moment model. The dynamic model, consisting of a set of differential and algebraic equations, is optimized using the simultaneous optimization approach in which all the state and input trajectories are parameterized. The resulting problem is subsequently solved by a non-linear programming algorithm. The optimal operation is realized by manipulation of the heat input to the crystallizer such that a maximal allowable crystal growth rate is maintained in the course of the process. Effective control of the crystal growth rate in batch crystallization processes is often crucial to avoid product quality degradation. To be able to effectively track the maximum crystal growth rate, the optimal heat input profile is computed on-line using the current system states that are estimated by an extended Luenberger-type observer based on CSD measurements. The feedback structure of the control framework enables the optimizer to reject process uncertainties and account for plant-model mismatch. It is demonstrated that the application of the proposed on-line optimization strategy leads to a substantial increase, i.e. 30%, in the amount of crystals produced at the batch end, while the product quality requirements are fulfilled. © 2009 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.