James B. Rawlings Research Group


Stephen M. Miller
Ph.D., University of Texas at Austin, 1993

Publications

1
Hazel B. Matthews, Stephen M. Miller, and James B. Rawlings.
Model identification for crystallization: Theory and experimental verification.
Powder Tech., 88(3):227-235, 1996.

2
Stephen M. Miller and James B. Rawlings.
Model identification and control strategies for batch cooling crystallizers.
AIChE J., 40(8):1312-1327, August 1994.

3
Stephen M. Miller, Hazel B. Matthews, and James B. Rawlings.
Model identification and control strategies for batch cooling crystallizers.
First International Particle Technology Forum, Denver, Colorado, August 1994.

4
Stephen M. Miller.
Modelling and Quality Control Strategies for Batch Cooling Crystallizers.
PhD thesis, The University of Texas at Austin, April 1993.

5
James B. Rawlings, Stephen M. Miller, and Walter R. Witkowski.
Model identification and control of solution crystallization processes: a review.
Ind. Eng. Chem. Res., 32(7):1275-1296, July 1993.

6
James B. Rawlings, Chester W. Sink, and Stephen M. Miller.
Control of crystallization processes.
In Allan S. Myerson, editor, Handbook of Industrial Crystallization, pages 179-207, Boston, 1993. Butterworth-Heinemann.

7
James B. Rawlings and Stephen M. Miller.
On-Line Control: Applications and Prospects of Model Based Control Schemes.
Third Annual Meeting of the Association for Crystallization Technology, Roche Pharmaceuticals, Nutley, New Jersey, March 1993.

8
Stephen M. Miller and James B. Rawlings.
Control strategies for batch cooling crystallizers.
Annual AIChE Meeting, Miami Beach, Florida, November 1992.

9
James B. Rawlings and Stephen M. Miller.
Controlling Particle Size in Batch Cooling Crystallizers.
Second Annual Meeting of the Association for Crystallization Technology, Eastman Chemical Co., Kingsport, Tennessee, February 1992.

10
Stephen M. Miller and James B. Rawlings.
An optimal control strategy for batch cooling crystallizers.
Annual AIChE Meeting, Los Angeles, California, November 1991.

11
Walter R. Witkowski, Stephen M. Miller, and James B. Rawlings.
Light scattering measurements to estimate kinetic parameters of crystallization.
In Allan S. Myerson and Ken Toyokura, editors, Crystallization as a Separations Process, pages 102-114. American Chemical Society, 1990.

12
Walter R. Witkowski, Stephen M. Miller, and James B. Rawlings.
Kinetic parameter estimation of crystallization processes.
Annual AIChE Meeting, San Francisco, California, November 1989.

Thesis Abstract

Modelling and Quality Control Strategies for Batch Cooling Crystallizers

Common quality requirements for crystallization products (e.g., ability to flow, dissolution rate, and aesthetic appeal) depend on the crystal size distribution (CSD). In some cases, such as for products to be used in photographic materials, size uniformity is so critical that the CSD is the primary consideration of a customer. In addition to customer requirements, a concern of the manufacturer is the CSD-influenced efficiency of downstream processes such as thickening and filtration. Whether the output of a crystallizer is the final product, the quality of which will be scrutinized by the consumer, or an intermediate product in a large process, there is economic incentive for reproducible control of the CSD.

This study focuses on the CSD control of seeded, batch cooling crystallizers. An open-loop, model-based control strategy that handles input, output, and final-time constraints has been developed and experimentally tested with a KNO$_3$-H$_2$O system in a fully-instrumented, bench-scale crystallizer. Compared to natural cooling, increases in the weight mean size of up to 48% were achieved through implementation of optimal cooling policies on the KNO$_3$-H$_2$O system.

The control scheme is applicable to crystallization with size-dependent growth rate, growth dispersion, and fines dissolution. It also permits flexibility in objective function formulation, allowing consideration of objective functions that take into account solid-liquid separation in subsequent processing steps.

Model identification plays an important role in a model-based control algorithm and is an integral part of this research. Uncertainty bounds on model parameter estimates are not reported in most crystallization model identification studies. This obscures the fact that the resulting models are often based on experiments that do not provide sufficient information and are therefore unreliable. This study provides a method for assessing parameter uncertainty and demonstrates the use of this assessment in experimental design. It is shown that measurements of solute concentration in the continuous phase and the transmittance of light through a slurry sample allow reliable parameter estimation. It is also shown that uncertainty in the parameter estimates is decreased by data from experiments that achieve a wide range of supersaturation. The model identification and control problems are connected through the issue of the sensitivity of the determined control policy to parameter uncertainty. A method to assess this sensitivity is presented.

For the KNO$_3$-H$_2$O system, it is shown that little benefit over that obtained from the open-loop optimal policy should be expected from using feedback in the control scheme. Nevertheless, a discussion is given of the potential need for feedback compensation in some systems, and suggestions are given for methods to incorporate feedback.

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University of Wisconsin
Department of Chemical Engineering
Madison WI 53706