(744g) Systematic Design of Supersaturation Controlled Crystallization Processes

Crystallization from solution is an industrially important unit operation due to its ability to provide high purity separation and is particularly used in the processing of expensive fine products. Batch cooling crystallization provides the advantages of being simple, flexible, and generally requiring less process development and investment than many other separation/purification techniques. However, despite the long history and widespread application of batch crystallization, there are a disproportionate number of problems associated with its monitoring and control. Many problems in downstream processes can be attributed to poor particle characteristics established in the crystallization step. The control objectives for batch crystallization processes can be defined in terms of product purity, crystal habit, morphology, average particle size, crystal size distribution, bulk density, product filterability, and dry solid flow properties [1]. On-line control during batch crystallization allows for improved crystal product quality, shorter process times, and reduction or elimination of compromised batches. Most of the product quality are directly related to the crystal size distribution (CSD) [2]. Even if some of the objectives can be expressed in terms of the moments of the distribution, knowing and predicting the entire shape of the distribution allow the design and adaptation of operating policies to achieve significantly improved product quality.

The paper presents the methodology for an efficient solution of the population balance equations using a combined quadrature method of moments (QMOM) and method of characteristics (MOCH) [3,4]. The coupled algorithm allows the prediction of the shape of crystal size distribution (CSD) throughout the entire process. The methodology provides a computationally efficient approach for the solution of generic PBEs, including size-dependent growth and primary or secondary nucleation mechanisms. The crystallization kinetic parameters are identified using a laboratory crystallization system based on crystal size distribution data obtained from off-line image analysis using the Sympatec's Quicpic equipment. The model is used to design the temperature profile and seed characteristics to achieve desired shape of the CSD. Although the benefits of supersaturation control basad direct design approaches have extensively demonstrated already in the literature, the operating curves (setpoints) for the supersaturation controller in practice are determined using trial and error procedures. The paper introduces a supersaturation control design parameter, based on which systematic design of the operating curve can be performed using a simplified model derived using the combined QMOM and MOCH. The approach is corroborated first through simulations. The designed operating curves are then evaluated in the experimental plant using an open-loop control strategy. The model with the efficient solution algorithm can also serve as a soft sensor for predicting CSD or as a computationally efficient approach for off-line design or on-line adaptation of operating policies based on complete CSD data. The approach is useful to achieve improved consistency of the product CSD and gives useful information for monitoring and designing the operating curves for the supersaturation controller. Experimental verification of the optimal operating policies on a laboratory scale will also be presented.

References:

[1] Wibow, C., and Ng, K. M. (2001). ?Design of integrated crystallization systems.? AIChE Journal, 47(11),2474-2492.

[2] Zhang, G. P., and Rohani, S. (2003) On-line optimal control of a seeded batch cooling crystallizer. ? Chemical Engineering Science, 58, 1887-1896.

[3] Hounslow, M. J., and Reynold, G. K. (1998). ?The population balance as a tool for understanding particle rate processes.? Kona Powder and Particle, 16, 1821-1832.

[4] Hounslow, M. J., and Reynold, G. K. (2006). ?Product engineering for crystal size distribution? AIChE Journal, 52, 2507-2517.