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N. Lawrence Ricker

N. Lawrence Ricker

Professor Emeritus of Chemical Engineering




  • B.S., University of Michigan, 1970.
  • M.S., University of California (Berkeley), 1972.
  • Ph.D., University of California (Berkeley), 1978.

Research Interests


  • Process control theory and applications
  • Process monitoring and fault detection
  • Nuclear fuel reprocessing material accountancy
Process Control and Optimization

As the chemical industry matures, companies are emphasizing waste reduction and the optimal use of raw materials and energy resources. Changes in process design are one way to improve efficiency. Other opportunities arise during normal operations.

For the last decade my group has been developing control algorithms for use in complex continuous and batch processes. Applications have included biological systems, municipal waste-treatment, and semiconductor materials production. The key idea is to develop a mathematical model that incorporates the dominant process features, then use the model directly in a control strategy. Such methods have come to be known as Model Predictive Control (MPC).

MPC offers significant improvements over conventional control methods. For example, the figure below compares a nonlinear version of MPC to a classical single-loop (SISO) strategy. The application is the Tennessee Eastman Industrial Challenge Process. The objective is to keep three variables within the limits shown as dashed horizontal lines. The strategies are equally good for product composition (%G and %H in the figure), but the conventional strategy (dotted lines) violates the limits on production. Also, it does a much poorer job of controlling reactor pressure, which is a critical variable from the point of view of safety and operating costs.

An outgrowth of this work is the MPC Toolbox for MATLAB (The MathWorks - co-authored with M. Morari), which is currently installed at over 1000 industrial and academic institutions world-wide. Current projects include the control and optimization of batch processes, and the use of "chemometric" techniques to derive the maximum benefit from process data.


Recent Publications


  • Muller, C.J., Craig, I.K., Ricker, N.L. Modelling, validation, and control of an industrial fuel gas blending system. Journal of Process Control 2011, 21(6): pp. 852-860.
  • Ricker, N.L. Predictive hybrid control of the supermarket refrigeration benchmark process. Control Engineering Practice 2010, 18(6): pp.  608-617.
  • Welz, C., Srinivasan, B., Marchetti, A., Bonvin, D., Ricker, N.L. Evaluation of input parameterization for batch process optimization. AIChE J. 2006, 52(9): pp. 3155-3163. 
  • Poochinda, K., Chen, T.C., Stoebe, T.G., Ricker, N.L. Structural, optical and electrical properties of GaN and InGaN films grown by MOCVD. J. Crystal Growth 2004, 272(1-4): pp. 460-465.
  • Poochinda, K. , Chen, T.C., Stoebe, T.G., Ricker, N.L. Simulation of GaN and InGaN p-i-n and n-i-n photo-devices. J. Crystal Growth 2004, 261(2-3):pp.  336-340. 
  • Chen, T. C., Johnson, M., Poochinda, K., Stoebe, T.B., Ricker, N.L. A systematic study on group III-nitride thin films with low temperature deposited via MOCVD. Optical Materials 2004, 26 (4): pp. 417-420.
  • Ricker, N. L. Using MATLAB/Simulink for data acquisition and control. Chem. Eng. Edu. 2001, 35(4): pp. 286-289.
  • Srinivasan, B., Primus, C.J., Bonvin, D., Ricker, N.L. Run-to-run optimization via control of generalized constraints. Control Engineering Practice 2001, 9(8): pp. 911-919.
  • De Jong, S, B.,  Wise, M., Ricker, N.L. Canonical partial least squares and continuum power regression. J. Chemometrics 2001, 15: pp. 85-100.
  • Johnson, M., Poochinda, K. Pearsall, T., Rogers, J.W., and N. L. Ricker . In-situ monitoring and control of multicomponent gas phase streams for growth of GaN via MOCVD. J. Crystal Growth 2000, 212: pp. 11-20.
  • Luke, J., Ricker, N.L. UO Lab:  Mass Transfer and Axial Dispersion in a Reciprocating-Plate Liquid Extraction Column. Chem. Eng. Edu. 1998, 32(3):  pp. 202-205.