James B. Rawlings Paul A. Elfers Professor Address/E-mail Program Affiliations Courses Education Fields of Interest Publications Awards & Honors Summary Files and Links For additional information, see my

Contact Information

2006 Engineering Hall 1415 Engineering Drive Madison, WI 53706

Tel: 608/263-5859 Fax: 608/265-8794 E-mail: rawlings@engr.wisc.edu

Program Affiliations

• Chemical and Biological Engineering

Courses

• CBE 430: Chemical Kinetics and Reactor Design
• CBE 470: Process Dynamics and Control
• CBE 660: Intermediate Problems in Chemical Engineering
• CBE 731: Computational Modelling of Reactive Systems
• CBE 770: Advanced Process Dynamics and Control

Education

• BS, The University of Texas at Austin
• PhD, UW-Madison

Fields of Interest

• process modelling dynamics and control
• nonlinear model predictive control
• state estimation and process identification
• particle technology

Publications

Chemical Reactor Analysis and Design Fundamentals James B. Rawlings John G. Ekerdt University of Wisconsin, Madison University of Texas at Austin

• Mastny, E. A., E. L. Haseltine, and J. B. Rawlings. Two classes of quasi-steady-state model reductions for stochastic kinetics. J. Chem. Phys., 127(9):094106, September 2007.
• Middlebrooks, S. A. and J. B. Rawlings. Model predictive control of Si-Ge thin film chemical vapor deposition. IEEE Trans. Semicond. Manuf., 20(2):114–125, May 2007.
• Pannocchia, G., J. B. Rawlings, and S. J. Wright. Fast, large-scale model predictive control by partial enumeration. Automatica, 43:852–860, 2007.
• Larsen, P. A., J. B. Rawlings, and N. J. Ferrier. Model-based object recognition to measure crystal size and shape distributions from in situ video images. Chem. Eng. Sci., 62:1430–1441, 2007.
• Mastny, E. A., E. L. Haseltine, and J. B. Rawlings. Stochastic simulation of catalytic surface reactions in the fast diffusion limit. J. Chem. Phys., 125(19):194715, November 2006.
• Middlebrooks, S. A. and J. B. Rawlings. State estimation approach for determining composition and growth rate of Si1−xGex chemical vapor deposition utilizing real-time ellipsometric measurements. Applied Opt., 45:7043–7055, 2006.
• Rawlings, J. B. and B. R. Bakshi. Particle filtering and moving horizon estimation. Comput. Chem. Eng., 30:1529–1541, 2006.
• Larsen, P. A., J. B. Rawlings, and N. J. Ferrier. An algorithm for analyzing noisy, in situ images of high-aspect-aspect ratio crystals to monitor particle size distribution. Chem. Eng. Sci., 61(16):5236–5248, 2006.
• Odelson, B. J., A. Lutz, and J. B. Rawlings. The autocovariance least-squares methods for estimating covariances: Application to model-based control of chemical reactors. IEEE Ctl. Sys. Tech., 14(3):532–541, May 2006.
• Odelson, B. J., M. R. Rajamani, and J. B. Rawlings. A new autocovariance least-squares method for estimating noise covariances. Automatica, 42(2):303–308, February 2006.
• Haseltine, E. L. and J. B. Rawlings. On the origins of approximations for stochastic chemical kinetics. J. Chem. Phys., 123(16):Art. No. 164115, October 2005.
• Haseltine, E. L., J. B. Rawlings, and J. Yin. Dynamics of viral infections: Incorporating both the intracellular and extracellular levels. Comput. Chem. Eng., 29:675–686, 2005.
• Haseltine, E. L. and J. B. Rawlings. Critical evaluation of extended Kalman filtering and moving
  horizon estimation. Ind. Eng. Chem. Res., 44(8):2451-­2460, April 2005.
• Haseltine, E. L., D. B. Patience, and J. B. Rawlings. On the stochastic simulation of particulate
  systems. Chem. Eng. Sci., 60(10):2627-­2641, 2005.
• Pannocchia, G., N. Laachi, and J. B. Rawlings. A candidate to replace PID control: SISO constrained
  LQ control. AIChE J., 51(4):1171-­1189, April 2005.
• Patience, D. B., P. C. Dell'Orco, and J. B. Rawlings. Optimal operation of a seeded pharmaceutical
  crystallization with growth-dependent dispersion. Org. Process Res. Dev., 8(4):609-­615, 2004.
• Tenny, M. J., S. J. Wright, and J. B. Rawlings. Nonlinear model predictive control via feasibility-
  perturbed sequential quadratic programming. Comp. Optim. Appl., 28(1):87-­121, April 2004.
• Pannocchia, G., S. J. Wright, and J. B. Rawlings. Existence and computation of infinite horizon
  model predictive control with active steady-state input constraints. IEEE Trans. Auto. Cont., 48(6):1002-1006, June 2003.
  
• Rao, C. V., J. B. Rawlings, and D. Q. Mayne. Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations. IEEE Trans. Auto. Cont., 48(2):246-258, February 2003.
• Haseltine, E. L. and J. B. Rawlings. Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics. J. Chem. Phys., 117 (15):6959-6969, October 2002
• Patience, D. B. and J. B. Rawlings. Particle-shape monitoring and control in crystallization processes. AIChE J., 47(9):2125–2130, September 2001.
• Rao, C. V., J. B. Rawlings, and J. H. Lee. Constrained linear state estimation – a moving horizon approach. Automatica, 37(10):1619–1628, 2001.
• James B. Rawlings. Tutorial overview of model predictive control. IEEE Control Systems Magazine, 20(3):38-52, June 2000.
• David Q. Mayne, James B. Rawlings, Christopher V. Rao, and Pierre O. M. Scokaert. Constrained model predictive control: Stability and optimality. Automatica, 36(6):789-814, 2000.
• Pierre O.M. Scokaert, David Q. Mayne, and James B. Rawlings. Suboptimal model predictive control (feasibility implies stability). IEEE Trans. Auto. Cont., 44(3):648-654, March 1999.
• Christopher V. Rao, Stephen J. Wright, and James B. Rawlings. On the application of interior point methods to model predictive control. J. Optim. Theory Appl., 99:723-757, 1998.
• Edward S. Meadows, Michael A. Henson, John W. Eaton, and James B. Rawlings. Receding horizon control and discontinuous state feedback stabilization. Int. J. Control, 62(5):1217-1229, 1995.
• James B. Rawlings and Kenneth R. Muske. Stability of constrained receding horizon control. IEEE Trans. Auto. Cont., 38(10):1512-1516, October 1993.
• 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.
• Kenneth R. Muske and James B. Rawlings. Model predictive control with linear models. AIChE J., 39(2):262-287, February 1993.
• John W. Eaton and James B. Rawlings. Model predictive control of chemical processes. Chem. Eng. Sci., 47(4):705-720, 1992.

Selected Awards, Honors and Societies

• Byron Bird Award for Excellence in a Research Publication, College of Engineering, UW Madison (2005)
• Computing in Chemical Engineering Award, CAST Division, AIChE (1999)
• Van Ness Lecturer, Rensselaer Polytechnic Institute (1999)
• Paul A. Elfers Chair in Chemical Engineering, University of Wisconsin (1995, 2000)
• NSF Presidential Young Investigator Award (1989)
• NATO Postdoctoral Fellow, Institute for System Dynamics and Process Control, Stuttgart, Germany (1985)

Summary

Chemical Process Monitoring and Control: Chemical processes are inherently nonlinear and must operate at their design constraints to achieve optimal economic performance. We have developed methods of moving horizon estimation and model predictive control to monitor and control the operation of chemical processes. This research has provided new theoretical results as well as practical, implementable methods for industrial application. This research is demonstrated in our own experimental facilities as well as through ongoing collaboration with several chemical and pharmaceutical companies.

We have established the Texas-Wisconsin Modelling and Control Consortium (TWMCC) to promote these collaborations. Graduate students involved in this research have many opportunities to present research results at consortium meetings and interact with industrial collaborators.

Particle Technology: The goal of this research is to better understand the processes and reactors that produce or change the size distribution of solid particles. Current research is directed toward developing accurate, on-line particle size distribution measurement using video microscopy and image analysis. Novel modelling techniques for these systems are under investigation, including stochastic differential equations and deterministic population balances.

Computer-Aided Modelling: Our group has developed Octave, a freely available, high-level computer language for numerical simulation and analysis of chemical engineering models. We use Octave in order to define models quickly, compute and analyze solutions, estimate model parameters from data, and solve controller design problems. You can find out more about Octave and the research activities in the group from the web site: http://www.che.wisc.edu/jbr-group.

Files and Links of Interest

• Octave: : Octave modelling language
• TWMCC

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