● Professor Thomas Bewley, University of California, San Diego.

On the convergence of boundary control strategies designed using ODE approximations of diffusive PDE systems.

This paper considers the convergence upon grid refinement of control strategies derived from ODE aproximations of diffusive boundary-controlled linear PDE systems. It focuses specifically on the Dirichlet boundary control of the heat equation as a canonicalmodel formore general diffusive PDE systems. It treats two classes of problems: the controllability problem (that is, the determination of a control distribution to move a system exactly from a specified initial state to a specified terminal state in finite time) and the state feedback control problem (that is, the determination of an optimal feedback rule u = Kx which minimizes some quadratic cost function J measuring both the state of the system and the control input), in the latter problem focusing specifically, for simplicity, on the infinite-horizon (that is, constant-gain) case. Both classes of problems require special attention beyond the usual considerations commonly known for the control of low-order ODE systems. Specifically, convergence of the control strategies upon refinement of the ODE approximation used in the controller calculation is not guaranteed. Note that the present study considers sine, finite difference, and Chebyshev discretizations, all of which provide consistent results, indicating that the results reported are not a spurious artificat of any particular numerical discretization.

 

● Professor Jan-Dirk Jansen, Delft University of Technology.

Model-based control of subsurface flow.

 An emerging method to increase the recovery from oil reservoirs is the application of measurement and control techniques to better control subsurface flow over the life of the reservoir. In particular the use of sensors and remotely controllable valves in wells and at surface, in combination with large-scale subsurface flow models is promising. Various elements from process control may play a role in such closed-loop reservoir management, in particular optimization, parameter estimation and model reduction techniques.

 

● Professor Biao Huang, University of Alberta, Edmonton.

Bayesian methods for control loop monitoring and diagnosis.

There exist many algorithms for control performance monitoring. There are also many algorithms available for process or instrument monitoring. There are, however, few methods available for synthesis of various monitoring technologies to form a diagnosing system for optimal decision making. This paper is concerned with establishing and demonstrating a novel probabilistic diagnostic framework for control loop monitoring. The new framework possesses a number of desired properties including, for example, probabilistic diagnosing procedure, flexibility in synthesizing different monitoring technologies, robustness in the presence of missing data or missing variables, ease of expansion or shrinking of the diagnosing system, ability to incorporate a priori process knowledge, and capability for decision making. As the backbone of the proposed framework, the emerging Bayesian methods are introduced and shown to be the appropriate tools. Several representative control loop diagnostic problems are formulated under the Bayesian framework and their solutions are demonstrated through examples.

 

● Dr. Ing. Tor Steinar Schei, Cybernetica AS, Norway.

On line estimation for process control and optimization applications.

 Design of Kalman filter type and moving horizon estimators for on-line estimation applications based on first principles models is reviewed. Important design issues are discussed, such as: model development; choice of process noise model and selection of model parameters for on-line estimation; use of asynchronous and delayed measurements; and off-line estimation of fixed but uncertain model parameters. The main conclusion, which is substantiated through application examples, is that robust and reliable estimation applications based on first principles models of considerable complexity, can be designed and implemented for use in an industrial environment.

 

● Professor James B. Rawlings, University of Wisconsin.

Coordinating multiple optimization-based controllers: New opportunities and challenges.

The status of using many, distributed optimization-based controllers for feedback control of large-scale, dynamic processes is presented and evaluated. We show that modeling the interactions between subsystems and exchanging trajectory information among subsystem model predictive controllers (MPCs) is insufficient to provide even closed-loop stability. The cause of this closed-loop instability is competition between the local agents.We next discuss the cooperative distributed MPC framework, in which the objective functions of the local MPCs are modified to achieve systemwide control objectives. This approach provides guaranteed nominal stability and performance properties, but at the cost of a high degree of communication between the local controllers. We next discuss the issue of taking advantage of the structure of the connections between the subsystems to reduce the required communication. The paper concludes by briefly presenting seven current and unsolved research challenges.

 

● Zoltan K Nagy, Mitsuko Fujiwara and Richard D Braatz.

Recent advances in the modelling and control of cooling and antisolvent crystallization of pharmaceuticals.

Although for decades nearly all pharmaceuticals have been purified by crystallization, there have been a disproportionate number of problems associated with the operation and control of these processes. The talk provides an overview of the recent advances in model-based and model-free (direct design) approaches to control the crystallization of pharmaceuticals, treating both antisolvent and cooling crystallization. A model-based combined technique, which simultaneously controls the antisolvent addition rate and the cooling profile is presented. A population balance model of the combined cooling-antisolvent addition system is developed and a moments model used in optimal control strategies with various objective functions. The simulation and experimental results show the advantages of the combined approach.

 

● Luis Alberto Ricardez Sandoval, Hector Marcelo Budman and Peter Lewis Douglas.

Simultaneous design of systems under uncertainty: A robust modelling approach.

In this paper, a new methodology to integrate process design and control for systems under uncertainty is proposed. Instead of using dynamic optimizations to estimate the system’s maximum variability, process stability and process constraints, this methodology applies a robust control approach to calculate bounds on these conditions. To illustrate the methodology the design of a mixing tank process is considered.

 

● V. Zavalla, C. Laird, L. Biegler.

A fast computational framework for large-scale moving horizon estimation..

Moving Horizon Estimation (MHE) is an efficient optimization-based strategy for state estimation. Despite the attractiveness of this method, its application in industrial settings has been rather limited. This has been mainly due to the inability to solve, in real-time, the associated dynamic optimization problems. In this work, a fast MHE algorithm able to overcome this bottleneck is proposed. The framework exploits the advantages of simultaneous collocation-based formulations and makes use of large-scale nonlinear programming algorithms and sensitivity concepts. The approach is demonstrated on a full-scale polymer process, where accurate state estimates are obtained and on-line calculation times are reduced dramatically.