Control-relevant Identification of Sheet and Film Processes
R. D. Braatz*
E. I. du Pont de Nemours & Co.

Sheet and film processes, which include coating, papermaking, and polymer film extrusion processes, are of worldwide industrial importance. Existing identification and estimation techniques require much more input-output data than are usually available for poorly conditioned large-scale sheet and film processes. The objective of this project is to exploit the inherent structure of these processes to improve numerical conditioning and the robustness of model and state estimates. This is leading to the development of an automatic identification procedure, where the model and an estimate of its accuracy are iteratively improved as opportunities for increased input-output testing become available.

Analysis and Control of Large-Scale Dynamic Neural Network Systems
R. D. Braatz,* E. Rios-Patron
Fulbright Program

Although neural networks have been heavily applied in the process industries, there have existed no general techniques for analyzing the stability and performance of these systems. Polynomial-time computable analysis tools are being developed that are applicable to dynamic neural network systems with arbitrary interconnections. The application of these tools for optimization-based nonlinear control is under investigation.

Analysis of Systems with Process Constraints and Time Delay Uncertainties
R. D. Braatz,* E. Rios-Patron
Fulbright Program

Process constraints, time delays, and model uncertainties are prevalent in large-scale industrial processes. Existing algorithms for computing robustness margins do not adequately address the effect of time delay uncertainties and process constraints on the overall closed loop stability and performance. An algorithm is being developed for mapping time delay uncertainties to equivalent finite-dimensional real parametric variations that can be analyzed using available techniques. The effect of process constraints on closed loop stability and performance is addressed using improved Lyaponov function techniques.

Pattern Recognition Approaches for Fault Detection and Diagnosis
R. D. Braatz,* L. H. Chiang
International Paper Co.

Pattern recognition techniques are being developed for the on-line detection and isolation of faults in large-scale industrial plants. These algorithms notify the process operator when abnormal process behavior has occurred and its likely cause, based on past data histories for which similar behavior has occurred. Subspace identification and operator-theoretic statistical methods are being investigated for improving the dynamic behavior of the developed techniques.

Globally Optimal Robust Reliable Control of Large-Scale Sheet and Film Processes
R. D. Braatz,* N. V. Sahinidis, J. G. Van Antwerp
E. I. du Pont de Neumours & Co.

We are developing computational approaches for designing globally optimal controllers for large-scale sheet and film processes. The resulting controllers are robust to inaccuracies in physical properties of the sheet or film and to faults and/or failures in measured and manipulated variables. One of the key ideas in these approaches is to exploit nonlocalized structural characteristics of the sheet or film.

Modeling and Control of Industrial Crystallizers
R. D. Braatz,* D. L. Ma, T. Togkalidou
Merck

Crystallization from solution is an industrially important unit operation because of its ability to provide high-purity separations. For efficient downstream operations (such as filtration or washing), control of the mean particle size, shape, purity, and the crystal size distribution can be critically important. This project focuses on the development of models for large-scale crystallizers, which incorporate fundamental physics of particle nucleation and growth as well as heat transfer and mixing. Techniques are being developed for crystallizer design and control that involve parameter estimation, optimal statistical design to characterize the crystallization system, and multivariable nonlinear control schemes for optimizing product quality.